SIMULATION OF SENSORINEURAL HEARING IMPAIRMENT
by
Paul Duchnowski
S.B., Massachusetts Institute of Technology, Cambridge (1987)
SUBMITTED TO THE DEPARTMENT OF
ELECTRICAL ENGINEERING AND COMPUTER SCIENCE
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR THE DEGREE OF
MASTER OF SCIENCE IN ELECTRICAL ENGINEERING
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
May 1989
Massachusetts Institute of Technology, 1989
Signature redacted
Signature of Au thor
Department of Electrical Engineering and Computer Science
Signature redacted
-
Certified by
Of -- Patrick M. Zurek
ignature redactedm i Supervisor
-
Accepted by
v
Arthur C. Smith
Chairman, Department Committee on Graduate Students
JULii1~
A R~ I I V'-s
Simulation of Sensorineural Hearing Impairment
by
Paul Duchnowski
Submitted to the Department of Electrical Engineering
and Computer Science on May 19, 1989 in partial
fulfillment of the requirements for the degree of
Master of Science in Electrical Engineering
Abstract
An algorithm to simulate the effects of sensorineural hearing impairment is investigated. It employs automatic gain control in independent frequency bands to
reproduce elevated audibility thresholds, loudness recruitment and reduced dynamic
range characteristic of this type of loss. The performance of three listeners with
severe sensorineural hearing loss on several speech intelligibility tests is compared
to that of normal subjects listening to the output of the simulation. These tests
include consonant-vowel syllable identification and sentence keyword identification
for several combinations of speech-to-noise ratio, frequency-gain characteristic and
overall level. Generally, the simulation algorithm reproduces intelligibility performance very well. There is a clear trend for the simulation to give better intelligibility
than observed for impaired listeners when a high-frequency emphasis places more
speech above threshold at higher frequencies. It is speculated that upward spread
of masking which is not accounted for in the algorithm is the suprathreshold effect
responsible for this discrepancy. It is also suggested that the general form of the
algorithm may be expanded to accomodate this effect.
Thesis Supervisor:
Title:
Patrick M. Zurek
Principal Research Scientist
3
Acknowledgements
So, as has been pointed out to me, this is one place in this document where I exercise
full editorial conrol. Wow! Power!
Kidding aside (for the moment), I would like to thank all of the members of the
Sensory Communications Group (a.k.a. the seventh floor gang) for their invaluable
assistance and advice throughout this project; they made it a much enriching and
enjoyable experience. Special thanks go to Pat Zurek for the insight and wisdom
he shared with me and for the patience, above and beyond the call of duty, which
he showed when the going was rough. I tip my hat to Julie Greenberg and Doug
Henderson, fellow thesis-finishers who shared their Latex, Macintosh and Graf skills:
looks like we're going to make it. Please forgive my apocalyptic pronouncements
and less than inspired renditions of Pink Floyd's greatest hits during the final hours.
I thank my subjects: PG, AL, PB, KG, DW and AW for their good humor in
face of lonely hours in front of a terminal inside a sound proof booth. They managed
to make it fun.
My parents were as usual a constant source of support and encouragement,
without them I would not be writing these pages. I can only hope to some day
return the love and friendship they showed.
I thank Suzy who did what friends do best: patiently listened to my griping and
then convinced me that nothing was my fault and that everything was going to be
all right.
Finally, I gratefully acknowledge the financial support of the General Electric
Foundation and the National Institutes of Health who at various stages funded my
studies.
This thesis is dedicated to my brother Michael,
my truest friend always.
Contents
Abstract
Introduction
Motivation
1.2
Background . . . . . .
1.3
.
7
7
.
Filtering . . . .
1.2.2
Noise masking
1.2.3
Center-Clipping.
1.2.4
Level expansion
8
.
1.2.1
.
8
.
9
9
10
Problem Statement
. .
Early Algorithms
12
. .
12
. .
13
. .
14
.
.
.
. .
.
The Center-Clipping Algorithm . . . . . . . .
2.1.1 Rationale . . . . . . .
. .. .. .
. .
2.1.2 Implementation . . . .. . . . . . . .
2.1.3 Results . . . . . . . . . . . . . . . . .
Frequency domain clipping . . . . .. .....
16
2.2.3
The AGC Algorithm
.
2.2.2
Description . . . . . . . . . . . . . . . . .
Perceptual effects . . . . . . . . . . . . . .
Attempted improvements . . . . . . . . .
17
.
2.2.1
18
.
2.2
12
.
2.1
19
Overview . . . . . . .
3.2
Functional blocks . . . . .
21
3.2.1
The Filterbank . .
21
3.2.2
.
20
3.1
Level detector . . .
24
3.2.3
Gain Computation
.
25
3.3
.
20
.
.
.
3
. . . . . .
Implementation . . . . . .
29
.
2
7
1.1
.
1
2
4
CONTENTS
4.1
Aim ......
35
4.2
Methods ...........................
.........................
35
Speech materials ..............
35
4.2.2
Speech-spectrum noise and frequency-gain characteristics
37
4.2.3
Simulation parameters ...........
38
4.2.4
Subjects . . . . . . . . . . . . . . . . . .
39
4.2.5
Procedure . . . . . . . . . . . . . . . . .
40
R esults . . . . . . . . . . . . . . . . . . . . . . .
42
.
.
.
4.2.1
49
5.1
A im . . . . . . . . . . . . . . . . . . . . . . . .
49
5.2
Methods......
49
.
Detailed Evaluation of AGC Algorithm
.......................
Speech materials . . . . . . . . . . . . .
49
5.2.2
Simulation parameters . . . . . . . . . .
50
5.2.3
Subjects . . . . . . . . . . . . . . . . . .
50
5.2.4
Procedure . . . . . . . . . . . . . . . . .
51
R esults . . . . . . . . . . . . . . . . . . . . . . .
53
5.3.1
CV Syllables
. . . . . . . . . . . . . . .
53
5.3.2
Sentences . . . . . . . . . . . . . . . . .
59
5.3.3
Simulation without recruitment . . . . .
67
.
.
.
.
.
.
.
.
5.2.1
Discussion
70
6.1
Evaluation of performance
70
6.2
Importance of recruitment
72
6.3
Conclusions
73
.
. . . . . . .
Simulation Program
77
A.1
Full code
. . . . . . . .
77
A.2
Program changes for no recruitment
85
.
A
31
35
5.3
6
. . . . . . . . . . . . . . . . . . . . . .
Pilot Evaluation of AGC Algorithm
4.3
5
. . . . . ..
.
4
Illustration
.
3.4
5
CONTENTS
B Consonant Confusion Matrices
6
87
Chapter 1
Introduction
1.1
Motivation
An accurate simulation of sensorineural hearing loss should prove of value from
both a theoretical and practical standpoint. It would allow researchers to study
in isolation the perceptual difficulties due to deficiencies in the inner ear. It would
separate these problems from those stemming from conductive loss or environmental
factors. It would also help to define the aspects of hearing loss that most affect the
perception of sound by the hearing impaired. It would provide not only insights into
promising compensatory processing but also a preliminary test of the effectiveness
of any such method. If speech processed by a proposed hearing aid and subjected to
the simulation proved more intelligible than without the compensation, one could
consider such a hearing aid as likely to be useful in practice. The value of the
simulation would stem from the ease and expediency with which the investigator
could use it and thus obtain a quick evaluation of his approach. The simulation
could also be used by medical personnel working with the hearing impaired to gain
insight regarding the effectiveness of rehabilitative techniques.
Finally, it would
allow the families and associates of hearing impaired persons to better understand
and appreciate, and thus deal with, the impairment.
1.2
Background
There have been four types of signal degradation proposed to simulate the effects
of hearing impairment. They are described here briefly.
7
CHAPTER 1. INTRODUCTION
1.2.1
8
Filtering
In this approach frequency-specific attenuation is used to reproduce the elevated
thresholds of the impaired subject for the normal listeners. This is accomplished by
a linear filter with an appropriately shaped frequency response. The method does
not address any suprathreshold effects such as recruitment.
Fabry and Van Tasell (3) used this approach to simulate the impairments of six
subjects with losses mainly in the high frequencies and tested the identification of
20 consonant-vowel syllables.
There was no significant difference in performance
between simulated normal and impaired ears for two of the six impairements subjects when percent correct scores were considered. Four impairments were judged
well matched when feature transmission error patterns were compared. Walden et
al. (22) performed a similar experiment also using syllables. They tested eight
unilaterally impaired subjects and found the patterns of feature recognition similar
while mean consonant recognition was generally lower in the impaired ear than in
the simulated normal ear.
Both sets of authors concluded that threshold shift alone is not sufficient to
explain the performance of impaired subjects. They also noted that there appears
to be no correlation between gross error scores and feature error patterns.
1.2.2
Noise masking
This is perhaps the most popular method which yields reasonable results. It employs
additive masking noise which elevates the thresholds of the normal-hearing subjects
to match the impairment.
The noise also produces abnormally rapid growth of
loudness above threshold (recruitment) similar to that seen in sensorineural impairment.
In the second part of the above cited study Fabry and Van Tasell used this
method to simulate the impairments previously simulated by filtering. Their surprising finding was that noise masking never proved successful when filtering did
not. This is in contrast to the consonant reception results of Zurek and Delhorne
(24) who also employed noise masking. Their experiments, described in some detail
CHAPTER 1. INTRODUCTION
9
in Chapter 4, showed close matching between the performance of impaired and
normal subjects for a wide range of impairments and simuli.
Whether successful or not, this method suffers from an obvious drawback: the
presence of the noise. It precludes simulation of severe or profound losses since
the noise required is unacceptably loud. Even at moderate levels subjects may be
reluctant to listen to prolonged presentations. The method is effectively limited
to losses of less than 60-70 dB HL. Another potential problem lies in the fact
that, physiologically, the roots of decreased intelligibility for noise masked normal
listeners appear to be different than for the hearing impaired (14).
1.2.3
Center-Clipping
Gagne and Erber (4) developed a two-channel device which subjects the input
to center-clipping and time/frequency jittering. The center-clipping function (see
Figure 2.1), described in detail in the following chapter, simulates the elevated
threshold and produces recruitment. The frequency jitter was designed to simulate
reduced frequency selectivity. The authors simulated three theoretical audiometric
configurations for a series of tests of PB-word recognition and vowel and consonant
identification. They reported results in agreement with the published data on
similar impairments.
They did not, however, conduct comparative studies with
actual hearing impaired listeners.
As Gagne and Erber themselves point out, there are many ways of distorting
speech that will decrease intelligibility. Lower perception scores achieved by simulated normals do not necessarily indicate that they perceive the stimulus just as
the impaired listener does. In fact, there appear to exist significant shortcomings in
their approach: the system seems to introduce spurious distortion which is unlikely
to be a correlate of an impairment (see Chapter 2).
1.2.4
Level expansion
Villchur (20; 21) devised a system which adjusts the level of the signal in 16 channels
to transpose the impaired subject's loudness relationships to the normal hearing
CHAPTER 1. INTRODUCTION
10
span.
This transposition was accomplished by projecting, in each channel, the
span of hearing of the impaired subject onto the span of hearing of the normal
subject. The lower limits were defined as thresholds of audibility. The upper limit
for normals was a 74-phon equal loudness contour; for impaired subjects it was a
frequency contour about 20-30 dB above the normals' level. The method would
thus simulate elevated threshold and recruitment.
Four unilaterally impaired subjects judged the effect of the simulation on speech
to be "similar" to "very similar" to the unprocessed speech heard in their impaired
ears. Villchur also used the algorithm to explain the reported resistance of impaired
listeners to white noise and their vulnerability to speech-spectrum noise.
The main deficiency of this method is its rather arbitrary definition of recruitment ranges which do not conform results of published studies of loudness growth
(6; 12). This problem is illustrated in section 3.2.4. Nonetheless, in the course
of this study this approach was judged as the most promising and the algorithm
described in Chapter 4 is based on it.
1.3
Problem Statement
An issue directly related to the simulation problem is the question of which deficiencies associated with impairment are most closely and directly correlated with
reduced speech discrimination ability. Possible candidates include: distortion of
the normal loudness relationships (recruitment), reduction in frequency selectivity,
greater spread of masking effects, poorer temporal processing, etc.
In this study several simulation algorithms were proposed and investigated.
Rather than attempting to simulate every possible type of deficiency associated
with hearing loss these algorithms sought to reproduce as faithfully as possible a
reduced set of these phenomena. This was done for several reasons.
First, with
more than a few aspects under simulation the problem becomes rather intractable.
Second, the extent to which some of these characteristics are present and influence
intelligibility is hard to quantify and still a matter of debate. Scharf and Florentine
(16), for example, point to decreased frequency resolution as a basic characteristic
CHAPTER 1. INTRODUCTION
11
of an impairment. Humes (8), on the other hand, speculates that it may simply
be a result of elevated thresholds and injudicious choice of stimulus levels by some
investigators.
On the other hand, elevated thresholds and recruitment are well
accepted (in fact, they are the defining) characteristics of sensorineural hearing
loss. Third, one of the goals of this work was precisely to establish the sufficiency
of elevated thresholds and recruitment for accurate simulation. By excluding some
of the features of loss from the simulation, we are at the outset conceding that
some elementary effects (masking, for example) may not be represented. One of the
aims, however, was precisely to find whether these features are crucial to explaining
reduced speech reception by the hearing impaired.
Speech perception was the main focus of this work. The primary motivation for
this choice was the simple fact that speech is arguably the most important acoustic
stimulus for humans and thus should be our first concern.
Chapter 2
Early Algorithms
During the course of this study several algorithms which might potentially meet
the criteria outlined above were explored. This chapter describes two which were
considered but subsequently found to suffer from significant shortcomings.
The
rationale behind them and the reasons for judging them unsatisfactory will be briefly
described here.
2.1
2.1.1
The Center-Clipping Algorithm
Rationale
This approach was motivated by the system described by Erber and Gagne (4). It is
based on the observation that the center-clipping non-linear transfer function shown
in Figure 2.1 reproduces both the threshold and recruitment effects associated with
hearing loss.
Thus, input signals whose amplitude is below the cutoff point are
attenuated to zero. For an appropriately chosen cutoff point this function would
therefore simulate the elevated threshold.
Furthermore, as the input amplitude
grows above the cutoff the rms value of the output grows faster than linearly and
asymptotically approaches that of the input. In this sense the function mirrors the
effects of the abnormally steep loudness growth experienced by impaired listeners.
Figure 2.2 shows as an example the effect of the center-clipper on a sine-wave at
three different amplitudes.
It is evident that without further processing the output of this function will
contain undesirable distortion due to the non-linearity. However, if the clipping is
applied to a band-limited signal the harmonics occuring at multiples of the input
components' frequencies can be made to lie outside the frequency range of the input.
12
CHAPTER 2. EARLY ALGORITHMS
13
output
-T
T
Input
Figure 2.1: Center-clipper transfer function
In that case post-filtering with a filter whose passband is limited to the range of the
input will remove the harmonic part of the distortion. It will not, however, affect
any distortion components inside the input frequency range.
2.1.2
Implementation
Erber and Gagne used the center-clipping approach in a two-channel system in
which the stimulus was divided into two frequency bands: above and below 500
Hz.
Each of the filtered signals was then subjected to center-clipping.
In an
effort to extend this approach I used a fourteen channel filterbank composed of
third-octave five-pole Butterworth filters covering the range from 50 Hz to 4.5 kHz.
Figure 2.3 shows the block diagram of the algorithm. Each of the bandpass signals
was passed through a center-clipper with the threshold set to match the impairment.
Subsequently each clipped signal was post-filtered to remove out-of-band harmonics.
The pre- and post-filters were identical i.e. H,(w) = K(w).
The procedure was implemented on a VAX 11/750 computer with the aid of
SPUD, a signal-processing software package written at the Sensory Communications
Group (13).
As an initial test several sentences were processed and played out
using a 16-bit DAC. The clipping thresholds were set to correspond to an auditory
threshold of 60 dB SPL for all channels, with sentences presented at an overall input
CHAPTER 2. EARLY ALGORITHMS
14
T
-T
r0\/
T
(
A
</'
<I
Figure 2.2: Effect of center clipping on sinewaves
level of 80 dB SPL.
2.1.3
Results
Upon listening to the output of the simulation it was discovered that while the
processing certainly resulted in lowered intelligibility it also produced a noticeable
and unexpected distortion that gave the output a "bubbly" quality. This effect is
undesired since it is not believed to be a characteristic of actual hearing loss.
The most probable cause of the "bubbly" artifact, we felt, was in-band intermodulation. Figures 2.4 and 2.5 illustrate this effect.
The top panels show a test waveform and its spectrum, respectively.
As is
apparent from Figure 2.5 the waveform is composed of three sinewaves at 0.93, 1.0
and 1.04 kHz.
The bottom panels show the result of center-clipping this signal
CHAPTER 2. EARLY ALGORITHMS
Filterbank
CenterClippers
-H (o
.Input
H 2((0)
H 1((0)
15
-
-+
T1
-
T2
..
T1
Post-filters
-
--
___
K, (w)
+K2((0)
output
K 14((0)
Figure 2.3: Center-clipping algorithm block diagram
with clipping thresholds placed at
15, about half of the maximum amplitude.
The clipped signal was post-filtered with a bandpass filter centered at 1 kHz with
bandwith of 250 Hz, identical to the corresponding filter used in the simulation.
As we can see from the spectrum of this signal the processing produced significant
inter-modulation products which remain not only within the bandwidth of the filter
but are intermixed with the original components. It is evident that no conventional
filtering scheme will alleviate the problem. While this distortion would probably
further degrade the intelligibility of speech, it is unlikely to correspond to actual
features of an impairment.
To ensure that the distortion was not an artifact of the sampled-data representation (i.e. aliasing), the test signals were upsampled by a factor of four and
the simulation was repeated. There was no noticeable difference in the quality of
the output. These observations lead to the conclusion that the undesirable bubbly
CHAPTER 2. EARLY ALGORITHMS
16
Original Signal
30
15
0
a>
4-0
-15
E
-30-
Clipped and Filtered Signal
15
0
-15
0
5
10
15
20
25
Time, milliseconds
Figure 2.4: Complex waveform illustrating the effects of clipping and post-filtering
in one band
effects, if they are due to intermodulation distortion, are an inherent effect of the
processing.
2.2
Frequency domain clipping
The preceding analysis suggests that the desired algorithm should suppress spectral
components that fall below threshold without introducing new components.
An
obvious approach is to apply the center-clipping function of Figure 2.1 to the
CHAPTER 2. EARLY ALGORITHMS
17
-
2000
1600
-
Original Signal
-
1200
800
-
0)
CZ
400
-r-t
I
I
0
I
~J
I
I
I
I
Clipped and Filtered Signal
400
200
iga
-r
0
0
0.5
1.0
1.5
2.0
Frequency, kHz
Figure 2.5: Spectra of waveforms from Figure 2.4 showing introduction of intermodulation distortion
spectrum of the input rather than to the input itself.
2.2.1
Description
Figure 2.6 shows the steps involved in this algorithm. To provide a running spectrum
of the stimulus, Short Time Fourier Transforms (STFT) of length 25.6 ms (512
points for 20 kHz sampling rate) were computed. These were spaced 6.4 ms apart
(i.e. they overlapped by 19.2 ms). Each STFT was subjected to center-clipping.
CHAPTER 2. EARLY ALGORITHMS
18
Since the algorithm was implemented digitally this meant that each spectral component magnitude was passed through the function in Figure 2.1. The thresholds for
different components could be set independently depending on the characteristics of
the impairment. These modified STFTs (MSTFTs) served as input to the GriffinLim algorithm (5) whose output constituted the simulated signal.
Input
Short-Time
-.- -- Fourier --.. o-
T
Transform
STFTs
Algorithm
MSTFTs
Griffin-Lim
output
T
Spectral Clipping
impairment
thresholds
Figure 2.6: Block diagram of the frequency domain clipping algorithm
The Griffin-Lim algorithm iteratively reconstructs a signal from the magnitude
of MSTFTs. It requires an initial estimate of the signal to be reconstructed. Since
we want to preserve the same phase as in the original stimulus, the initial guess
used was the unprocessed waveform. The procedure yields a signal such that the
total squared error between the magnitude of its STFT and the input MSTFT
magnitudes is minimized.
2.2.2
Perceptual effects
The same sentences which were used to evaluate the time domain clipping algorithm
were processed according to this algorithm.
The spectral distortion was largely
removed. However, the bubbly effect was only slightly diminished.
In order to investigate the origin of this quality several test signals were processed. These were: 1) an amplitude-ramped square wave; 2) a frequency-modulated
CHAPTER 2. EARLY ALGORITHMS
19
square wave; and 3) a frequency and amplitude-modulated square wave. They were
chosen to test the theory that the effect which is mostly heard in the vowels is
associated with the alteration of voice pitch. However, for none of these signals was
the effect heard after processing. Further, examination of the spectrograms of the
processed stimuli revealed nothing that would account for this effect.
2.2.3
Attempted improvements
There was a possibility that the bubbly effect was due to time-aliasing. Clipping
the STFT is equivalent to applying a time varying and oddly shaped but very
steep filter. One would expect that in the time domain this implies a convolution
with a long impulse response.
It seemed plausible that a longer DFT than the
one used originally would be necessary to accurately represent the desired output.
To test this theory each 512-point frame was padded with zeros to 4096 points.
Nonetheless, no significant changes in the output were observed. The Griffin-Lim
algorithm never explicitly finds the inverse transform of the modified STFTs so the
potential for time aliasing is probably smaller and less predictable. It appeared not
to be a factor in this case.
By a similar rationale the algorithm was altered to shift the transform windows
by only 16 points. This would presumably improve resolution and more faithfully
reproduce transitions in the speech waveform (the distortion was most apparent in
the initial portions of vowels.) However, little change was observed in either the
visual appearance or the sound of the output waveform.
We don't have a good explanation for the bubbly effect. It might be partially
related to the fact that the distance measure used by the Griffin-Lim algorithm to
converge on the estimated output is a global one. Therefore the algorithm is prone
to make "local" errors. For example, not infrequently the amplitude of the output
of the simulation (after the spectral center-clipping) was observed to exceed that of
the input in portions of the signal.
Since both of the above algorithms showed this artifact no tests with subjects
were carried out. The algorithm described in the next chapter turned out to merit
closer inspection.
Chapter 3
The AGC Algorithm
This algorithm was based on the work of Vilichur described in Chapter 1.
In
a similar fashion it employs automatic-gain-control expansion to provide leveldependent channel gains.
It explicitly simulates elevated thresholds, recuitment
and reduced dynamic range.
3.1
Overview
Figure 3.1 shows the block diagram of the algorithm. In the first stage the input
is passed through a filterbank which divides it into N independent channels. Subsequent processing of all channels follows the same pattern. First, the short-time
RMS level of the signal as a function of time is determined in each channel. The
level waveform then serves as input to the gain computation block. Here, using parameters of the impairment, the relation of the input level to the impaired threshold
is used to determine the appropriate attenuation for the particular channel. Since
the level changes as a function of time the necessary attenuation will also change
with time. Each channel signal is then multiplied by its computed gain signal. The
modified channels are summed to produce the output.
In the digital implementation used in this study the above processing applies to
the input on a point-by-point basis. In fact, one may think of the gain computation
block as creating a gain curve which then multiplies, point-by-point, the channel
waveform.
In the following section the implementation of the major functional blocks and
the factors which influenced the choice of various parameters of the simulation are
described.
20
CHAPTER 3. THE AGC ALGORITHM
Level
Detector
Fiterbank
---a~~
1
Gain
Computation
2
channel 2
Level
t wDetector
--- HN (0
Gain
Computation
H()channel 1
Level
Detector
-- H2 (C)
21
Channel N
otu
Gain
hComputation
N
_C
Figure 3.1: Block diagram of the AGC algorithm
3.2
3.2.1
Functional blocks
The Filterbank
The bandwith of the filterbank was chosen to cover the frequency range from
50 Hz to 4.5 kHz.
The upper cutoff was largely determined by the fact that
available test stiun-Ai had been low-pass filtered at 4.5 kHz prior to digitization.
The bandpass filters used were approximately third-octave except for the five at
the lowest frequencies whose bandwiths were 100 Hz. With these requirements 14
filters were required to fill the desired span of frequencies. Table 3.1 lists the center
frequencies and bandwiths of all filters.
The filters were synthesized using a prototype three-pole Butterworth low-pass
CHAPTER 3. THE AGC ALGORITHM
Filter
22
1
2
Center
Frequency (kHz)
0.100
0.200
Bandwidth
(Hz)
100
100
3
0.300
100
4
0.400
100
5
6
7
8
0.500
0.625
0.800
1.025
100
150
200
250
9
10
11
1.300
1.600
2.000
300
300
500
12
13
2.550
3.225
600
750
14
4.000
1000
Table 3.1: Filterbank characteristics
filter with cutoff at 25 Hz. Modulating its impulse response with cosine waves at
increasing frequencies produced a series of filters with impulse responses
hk(t) = 2h(t) cos(27rfAt + iAk),
where hk(t) is the impulse response of the kth filter, h(t) is the impulse response of
the prototype filter and fk is the desired center frequency of the resulting bandpass
filter. Its bandwidth is twice that of the prototype, in this case 50 Hz. By adding
the impulse responses of appropriate adjacent filters the filters of Table 3.1 were
obtained.
This synthesis method was chosen to take advantage of the approach developed
by Schafer and Rabiner (15) which allows us to minimize the ripple in the combined
frequency response of the filters in the filterbank. By appropriately choosing <kA,
the phase of the modulating cosine, we may also be able to obtain a very nearly
linear phase characteristic. In this case both of these goals were accomplished for
<4k chosen such that the modulating cosines were all delayed by 1.3 ms; for example
CHAPTER 3.
(I
= -58.5*
THE AGC ALGORITHM
23
where f2 = 125 Hz.
2
0
M
V
-
-
CZ
-
-4
--------
---
-6
-8
-101'
0
1.0
2.0
3.0
4.0
5.0
Frequency, kHz
Figure 3.2: Total magnitude of the filterbank's frequency response
Figure 3.2 shows the magnitude of the frequency response of this filterbank. It
is apparent that the maximum ripple is much reduced from the simple filterbank
used initially (see Figure 4.5), from about 5 dB peak-to-peak to only about 0.5 dB.
Figure 3.3 illustrates how successful the filterbank was in achieving linear phase.
The top panel shows the waveform of the syllable /ba/. The bottom panel shows
the result of passing this waveform through the filterbank, summing the outputs
and shifting by 1.3 ms to compensate for the expected delay of the filters. The
filterbank clearly preserves the fine structure of the signal.
Although the phase of a sound stimulus is widely regarded as relatively unimportant, there is no guarantee that it is also unimportant when the signal is subjected
to nonlinear processing. Through careful design of the filterbank we are assured
that the simulation will be free of artifacts from at least that part of the processing.
CHAPTER 3. THE AGC ALGORITHM
24
Original
100
60200
-20-603 -100-
Filtered
100 60200-20-60-100-
I
0
0.2
I
0.4
I
0.6
I
0.8
I
1.0
Time, seconds
Figure 3.3: Effect of the filterbank on the syllable /ba/
3.2.2
Level detector
The level detector's function is to provide a running estimate of the intensity in
the input signal. For the purposes of this algorithm it is important that the
level detector track variations within syllables (tens of milliseconds) but not track
variations at the fundamental frequency rate (10 msec or less). To implement this
function, the instantaneous power Ek[n] in a given channel k is first found by simply
squaring the channel signal ck[n], i.e. Ek[n] = Ick[n]1 2 . These powers then have to
CHAPTER 3. THE AGC ALGORITHM
25
be smoothed to produce the short-term band levels ek[n]:
ek[n]=10log
z
w[n-m]E[m]
mn=-oo
The function w[n] is the smoothing window. In this case this was chosen to
be a noncausal rectangular window of length 20 msec (effective bandwidth ~ 50
Hz).
As reported by Bustamante (1) this window successfully eliminates pitch
related fluctuations while preserving syllabic variations. By using a window which
is symmetrical in time we also eliminate the delay between the level-detector output
and the input signal.
3.2.3
Gain Computation
This is, arguably, the most important part of the algorithm.
By appropriately
choosing the transfer function of this block we seek to reproduce the elevated
thresholds and abnormally steep loudness growth (recruitment) observed in hearing
impairments.
Description
Figure 3.4 illustrates the approach.
The dashed curve shows loudness growth
experienced by normal listeners as a function of the stimulus intensity. In general
this will be a straight line above the normal threshold of audibility labelled T.
Typical impaired loudness growth as might be measured with loudness balancing is
represented by the heavy line. At impaired threshold (Ti) the perceived loudness
equals that of the normal listener at his threshold.
For the range of stimulus
intensities between T; and T, the loudness grows faster than linearly. This is the
phenomenon of recruitment, therefore T,. will be called the threshold of recruitment.
For intensities above T, impaired loudness growth matches the normal curve. This
implies so-called complete recruitment.
In order to duplicate for the normal listener the impaired loudness growth the
algorithm has to scale the input signal such that the output intensity is related to
the input by the bold line. The double arrow indicates the amount of attenuation
CHAPTER 3. THE AGC ALGORITHM
A
26
output
intensity (dB)
Tr
E
-0
~-
7
-
-
-
attenuation
factor
+n
i
Tr
input
intensity (dB)
Figure 3.4: Gain computation transfer function. See text for details
(in dB, at a particular input intensity) necessary to accomplish this. As expected,
the attenuation decreases with increasing input level until at T,. the gain becomes
one. The attenuation used for signals whose level falls below threshold has to bring
them below the normal threshold.
The exact value is unimportant.
In practice
the algorithm essentially throws away most of the signal except for the 5 dB range
immediately below threshold. In this range the attenuation increases significantly
but some signal is still passed through. This is done to avoid a possible abrupt
onset of the signal when its level crosses the threshold which might be audible as
high frequency transient distortion.
This function may be easily adapted to include lowered threshold of discomfort
experienced by some hearing impaired listeners (17).
All that is necessary is to
CHAPTER 3.
THE AGC ALGORITHM
27
extend the curves further. At some point below the normal threshold of discomfort
the impaired curve will begin to exceed the normal curve. Now the distance between
the curves may be taken as the amount of amplification which will bring the
stimulus up to the normal discomfort threshold. Due to difficulties with a consistent
definition of discomfort threshold this aspect of the simulation was not tested.
Given this method, the algorithm needs two impairment parameters for each
channel: threshold of audibility and threshold of recruitment.
Throughout this
study tone detection thresholds were used for the former. The choice of the latter
is described in the following section.
Characterization of recruitment
Carver (2) describes four patterns of recruitment that have been observed. Of these,
the linear pattern of Figure 3.4 and the asymptotic pattern are by far the most
common. They tend to differ realtively little and only at the end of the recruitment
range. Stevens (18) suggests that there is only one pattern and the differences in
shape are a result of averaging. Only linear recruitment was simulated here.
Ideally, the threshold of recruitment would be obtained from actual measurements of the impaired function.
and not measured.
In practice, these data are often not available
More importantly, for bilateral impairment with thresholds
above normal for all frequencies it is impossible to use the balance test to measure
the recruitment.
Other methods are being developed (7) but none is yet widely
available.
Hallpike and Hood (6) have measured recruitment in over 4000 subjects. Their
data strongly suggest that the range of recruitment is a fairly orderly function of
the degree of loss. Their observations, which are in general agreement with those
of Miskolczy-Fodor (12), are summarized in terms of the angle (steepness) of the
recruitment curve. This angle a (in degrees) between the recruitment function and
horizontal axis is well approximated by
a = 47 + 0.45(T 1 - Tn)
where Ti and Tn are in dB, which predicts increasing slope for more severe im-
CHAPTER 3.
THE AGC ALGORITHM
28
pairments, as commonly observed. After appropriately converting this metric to
range of recruitment (by simple geometric relations) we obtain the threshold of
recruitment as follows:
T = tan(a)(T -
Tn)
tan(a) - 1
For example, for Tn=10 dB SPL and T=70 dB SPL, T,.=94 dB SPL (recruitment
range of 24 dB). Figure 3.5 shows the recruitment range as given by the above
equation as a function of the impaired threshold. The normal threshold was assumed
to be 0 dB SPL for this plot. The plot will in fact remain the same if the abscissa
is taken as the difference between the impaired and normal thresholds.
I
I
I
I
I
I
I
I
I
40
-o
30
1
Co
C
20
Ei
=3.
10
01
0
20
40
60
80
100
Impaired Threshold, dB SPL
Figure 3.5: Recruitment range as a function of T; T,., assumed to be zero
For very small and very large impairments unrealistically small recruitment
ranges are predicted. In order to avoid this problem the algorithm allows a minimum
recruitment range of 15 dB.
CHAPTER 3.
THE AGC ALGORIT HM
29
This method of calculating the recruitment range was chosen since it is believed
to be a more accurate representation of the loudness growth than the criterion
employed by Villchur as described in section 1.2.4. In that study (20; 21) a single
expansion ratio is computed for each frequency band based on the ratio of the
normal span of hearing to the impaired span of hearing. These are defined in each
band as the difference between the threshold of audibility and an equal loudness
contour. For normal subjects this contour is chosen as the 74-phon equal loudness
contour. The impaired contours shown by Vilichur are generally about 20-30 dB
above that. For impaired and normal thresholds of T and T, respectively and the
equal loudness levels of Xi and X, the expansion ratio is (X, - T.)/(X - Ti).
After the input signal's level is expanded with this ratio, attenuation is used to
simulate the elevated threshold. Figure 3.6 shows an example of the resulting gain
curve (bold line) based on thresholds at 1000 Hz in one set of charcteristics shown
by Villchur. Here T = 55 dB, X = 90 dB, T, = 8 dB and X, = 74 dB. It is
contrasted with the gain curve which would be employed by the AGC algorithm
under these conditions. The AGC curve shows steeper loudness growth as well as
saturation, i.e. complete recruitment and normal loudness growth above a certain
threshold. Villchur's expansion is shallower and continues indefinitely resulting in
overrecruitment. These differences are typical. Based on available data we believe
the AGC algorithm reproduces abnormal loudness growth more faithfully.
3.3
Implementation
The simulation was implemented on a VAX 11/750 computer.
C-language program which includes all of the functions of Figure
The code of the
3.1 is included
as Appendix A. The filterbank was synthesized beforehand using SPUD (13) and
the impulse responses were stored for access by the program. Filtering was done by
means of Fast Fourier Transform. The input stimuli were either digitized and stored
before processing or generated digitally.
Likewise, the output of the simulation
was in digital form. The signals were played out using a 16 bit Digital-to-Analog
converter and a Crown adjustable amplifier which set the output level and acted as
CHAPTER 3. THE AGC ALGORITHM
30
output
level
(dB)
-
-
100
80
-
120
-
-I
20
-
40
-'
-'
-
60
I
20
40
60
80
I
I
W,
100 120
input level (dB)
Figure 3.6: Comparison of the AGC algorithm gain curve and that of Villchur's
simulation
the headphone driver. For all tests the output was presented to TDH-39 headphones
mounted in Grason-Stadler .001 circumaural cushions. The full implementation is
diagramed in Figure 3.7.
The DAC's input range was -32768 to +32767. To minimize quantization noise
the stimuli were kept as close to full range as practical and the overall level was
adjusted externally. The DAC output 0.1526 1V/ unit and the phones nominally
produced 6.325 mPa
/
mV. Therefore the relation between the digital signal level
DA, analog gain G and the sound pressure level at the headphones, SPL,,
was:
SPLout = Dk + G + 33.7
where all quantities are in dB. The simulation program takes this into account by
CHAPTER 3. THE AGC ALGORITHM
31
VAX
sound
A/D
Disk
-WSimulation
Program
:PDAC
Gain
TDH-39
Figure 3.7: Implementation block diagram
taking the intended sound level of the input as a parameter. It then calculates the
necessary analog gain and converts the impairment thresholds from dB SPL to the
digital signal levels. For example: a digitized syllable with RMS overall level of
3276 (a tenth of full range) has D1 = 70.3 dB. If it is to be played out at 72 dB SPL
then G = -32 dB. A threshold of 60 dB SPL will then map to a "digital" threshold
of 58.3 dB. Naturally both the unprocessed and processed stimuli have to be played
using the same analog gain setting (unless digital scaling is used as well).
3.4
Illustration
Figures 3.8 and 3.9 show an example of the effect of the algorithm on the syllable
/bi/.
The top panel of Figure 3.8 shows the unprocessed digitized utterance. Its
RMS level is 1638. This syllable was processed by the algorithm assuming an overall
output level of 94 dB SPL and using the thresholds of subject PG (see Figure 5.1.)
The output of the simulation is shown in the lower panel.
Figure 3.9 illustrates the steps in the processing.
The top panel shows one
channel of the input (i.e. the syllable), in this case channel 3: frequency range 250
to 350 Hz. The second panel shows the output of the level detector.
Note that
the scale has been converted to dB SPL given the assumptions of the preceeding
paragraph. The impaired threshold in this frequency band is 76 dB SPL, the normal
threshold is 23 dB SPL. This implies that the threshold of recruitment is 104 dB
CHAPTER 3. THE AGC ALGORITHM
32
Unprocessed /bi/
-
6k
I
I
I
-
3k
I
L [[[III
I
Ia
0
+
-o
II1.1I
1.
III
II
E
-3k
I I I
-
I
II 'F"
I
I
I'll
liii.'
1k
I,
0
Simulated /bi/
111.6 L
vp
-1k
0
100
200
300
400
Time, milliseconds
Figure 3.8: Effect of the AGC algorithm on the syllable /bi/. See text for relevant
parameters
CHAPTER 3. THE AGC ALGORITHM
33
SPL. Since the peak level is only about 99.5 dB normal loudness is never reached
with this simulation in this channel. The third panel shows the output of the gain
computation block for this channel. As expected, the gain is zero for those parts
of the input where the level falls below 76 dB. The bottom panel shows the output
for this channel obtained by multiplying the channel input by the gain curve.
CHAPTER 3. THE AGC ALGORITHM
34
input
-
3k
-
1k
0-
-1 k
-3k
-J
a(/,
m
2
80
level
40
V
0
00.3
Cu
-
gain
-
output
0.2
0.1
0
1k
E
0
-1k
0*
100
200
300
400
Time, milliseconds
Figure 3.9: Steps in the simulation of the syllable /bi/: channel 3, T= 76 dB SPL,
T, = 23 dB SPL
Chapter 4
Pilot Evaluation of AGC Algorithm
4.1
Aim
As an initial test, the AGC algorithm was used to simulate the impairments described by Zurek and Delhorne (24). They compared consonant reception in noise
by hearing impaired listeners to that of normal subjects whose thresholds were
elevated by the addition of spectrally shaped noise. In that study subjects were
placed in one of five categories according to the shape of their audiograms.
For
each category of impaired subjects an "average" audiogram was determined. The
normal subjects' thresholds were then made to match the impaireds' by varying
the spectral shape of the masking noise.
The losses were mild to moderate, all
less than 70 dB. The intensity of masking noise necessary to simulate more severe
impairments would have been uncomfortable.
In the current study the same stimuli as those used by Zurek and Delhorne were
altered according to the AGC algorithm to simulate each of the five average loss
patterns. The performance of two normal hearing subjects with these simulation
stimuli was compared to that of the original hearing impaired subjects as an initial
indicator of the accuracy of the simulation.
4.2
4.2.1
Methods
Speech materials
The stimuli used were the same consonant-vowel syllables (CVs) used by Zurek and
Delhorne. Twenty four consonants (b,tf,d,f,g,h,d3 ,k,l,m,np,r,s,J, t,0,v,w,hw,5,y,z,3)
were paired with three vowels (a,i,u) for a total of 72 syllables. They were spoken by
35
CHAPTER 4. PILOT EVALUATION OF AGC ALGORITHM
36
a male speaker and recorded in a quiet/anechoic setting. These recorded syllables
were then low-pass filtered at 4.5 kHz, sampled at 10 kHz, and stored on a computer
disk. They were played back using a 12-bit D/A converter and a 4.5 kHz low-pass
filter. Figure 4.1 shows the block diagram of the experimental setup used in the
previous study for testing the hearing impaired subjects and noise-masked normals.
The syllables were presented to the point labeled "CVs" in that diagram. Figure 4.2
shows the block diagram of the processing used in the present experiment; here the
syllables were introduced at the "CVs" node still in their digitized form.
0
CVs
Frequency-Gain
Characteristic
SNR
Overall
+Multifilter
G a In
Level
Gain
SSN
Masked
Normal
-White
Noise
Multifliter
Threshold Noise
Shaping
r
TDH-39
paired
Figure 4.1: Block diagram of Zurek and Delhorne experimental setup
CHAPTER 4. PILOT EVALUATION OF AGC ALGORITHM
Frequency-Gain
Characteristic
Cvs
S NJRSNR+Filter Digital
SSGain
Simulation
Program
37
Overall
Level
Digital
Gain
DAC
Gain
TDH-39
Figure 4.2: Block diagram of the pilot experiment setup
4.2.2
Speech-spectrum noise and frequency-gain characteristics
In the Zurek and Delhorne study the interference noise was generated by analog
equipment and added to the CVs during presentation. This was continuous Gaussian noise, filtered to have the spectral shape of babble, hence the designation
"speech-spectrum noise" (SSN). For the present implementation of this simulation
the noise had to be added to the CVs while they were in digital form. White Gaussian noise was generated digitally and spectrally shaped with a filter whose frequncy
response magnitude, shown in Figure 4.3, was chosen to match the spectrum of the
SSN used by Zurek and Delhorne.
Two frequency-gain characteristics were employed. For the flat characteristic
uniform gain was applied to the CV-plus-noise stimulus.
For the high-frequency
emphasis (HFE) characteristic the gains of the multifilter used by Zurek and Delhorne were chosen so that the SSN was transformed into pink noise. For the present
study the syllables were digitally filtered prior to simulation with a filter whose
frequency response magnitude, shown in Figure 4.4, was chosen to match Zurek
and Delhorne's HFE filter.
CHAPTER 4. PILOT EVALUATION OF AGC ALGORITHM
40
I
I
I
I
I
I
I
I
I
I
I
I
38
I
30
20
V
(D
V:
0M
10
0I
-10
-20
-30 0
I
1.0
I
2.0
I
3.0
4.0
I
5.0
Frequency, kHz
Figure 4.3: Frequency response of speech-spectrum noise shaping filter
4.2.3
Simulation parameters
Since this evaluation was performed relatively early in the development process
several parameters of the algorithm used in the simulation were different from those
characterizing the final version as outlined in the previous chapter. The smoothing
window (for level computation) was 10 ms long as opposed to 20 ms eventually
decided on. The filterbank used was simply a collection of third-octave fifth-order
Butterworth filters (slopes = 30 dB/octave) with response magnitudes intersecting
at half-power points. The frequency response magnitude of this filterbank is shown
in Figure 4.5.
It has larger ripple than the filterbank used in the final version.
Finally, the recruitment range was a uniform 30 dB for all channels regardless of
the magnitude of the loss.
The shorter smoothing window is unlikely to have had a large effect. It was
observed that the shape of the waveform, particularly the consonant part, was
not greatly affected by changing the window to 20 milliseconds. Potentially more
CHAPTER 4. PILOT EVALUATION OF AGC ALGORITHM
39
16
1 21
-0
8
a;
-'
CT)
:|2
4
0
-4
-8 C
1.0
2.0
3.0
4.0
5.0
Frequency, kHz
Figure 4.4: Frequency response of the high-frequency emphasis filter
significant problems arise from the ripple since it produces varying amplification
near the overlap frequencies. The recruitment range, while admittedly somewhat
arbitrary, is consistent with the average ranges observed (18; 19). Furthermore, the
thresholds simulated are averages of several audiograms and so it is not unreasonable
to use an "average" recruitment range.
4.2.4
Subjects
The impaired subjects are described in detail in the aforementioned paper. Figure 4.6 shows average tone-detection thresholds of the subjects in each category.
These average thresholds were used in the simulation. For this pilot study the author
and another member of the laboratory served as the normal-hearing subjects. Tests
in the laboratory confirmed that their thresholds were within the normal range.
CHAPTER 4. PILOT EVALUATION OF AGC ALGORITHM
40
5
0
M
-5
c0
C
10
15
-20
0
1.0
2.0
3.0
4.0
5.0
Frequency, kHz
Figure 4.5: Total frequency response of the filterbank
4.2.5
Procedure
The conduct of the experiment is described in Zurek and Delhorne. It is repeated
here for convenience and because it was used again (with minor modifications) in
the experiments discussed in the next chapter. Unless otherwise noted the following
applies to both the previous and current studies.
The subjects were tested while seated before a computer terminal inside a soundproof room. Each experimental run comprised the presentation of a set of 72 CVs
at a particular SNR, frequency-gain characteristic, and overall level. The subject's
task was to identify each syllable as it was presented.
time limit for responding.
There was effectively no
Acceptable responses were in the form of an English
alphabet code for the consonants and the characters "a", "i", and "u" for the
vowels. The code followed the IPA alphabet except where the latter contained nonEnglish characters. For those consonants the codes used were: ch=/tf/, j=/d3/,
sh=/f/, th=/0/, wh=/hw/, xh=/6/, and zh=/ 3 /. To avoid possible confusion the
CHAPTER 4. PILOT EVALUATION OF AGC ALGORITHM
I
-
100
I
I
I
41
1
GrouplI, 3Ss
Average Threshold
50
-
4%4..
-.
0100
Normal Threshold
00
-
-J
C0
.
Group 111, 3 Ss
r,
.%
.%
50-
50
GroupllV, 2Ss
0
0-
-F-
100
C
00
-
Grou p V, 5 Ss
50
50
-
(I)
4%
0
0I I
I
0.25
1.0
4.0
I
I
0.25
1.0
Frequency, kHz
Figure 4.6: Tone detection thresholds of the impaired subjects
4.0
-.C
CHAPTER 4. PILOT EVALUATION OF AGC ALGORITHM
code for
/j/
42
was "y." The subjects had available during the experiment a list of all
valid responses along with illustrative words containing the sounds. Both normal
subjects were familiar with the response code. Zurek and Delhorne reported that
their subjects seemed to have no problems mastering it.
No trial-by-trial feedback was given. The next syllable was presented 0.5 seconds
after the response to the previous stimulus. When invalid responses were made the
subjects was prompted to choose an acceptable answer (i.e. one in the response set).
The subject could waive responding on any trial by typing "return." The stimulus
presented on that trial was returned to the pool of unplayed syllables to be replayed
later in the same run. In this way valid responses for all syllables were obtained.
4.3
Results
Figures 4.7- 4.11 show the percent of consonants identified correctly as a function of
SNR for each of the five impairment categories. The SNRs employed in the present
study were -10, -5, 0, and 5 dB as well the "Quiet" (Q) condition: SNR > 20dB. The
bold solid lines and the bold crosses represent the average performance of the two
normal subjects. For the flat frequency-gain characteristic one of the two normal
subjects (PD) was tested once at each SNR and overall level while the other (PZ)
was tested once in "Quiet" and at -10 dB SNR. For the HFE characteristic both
normals were presented stimuli once in the
Q condition
and once at 0 dB SNR for
each overall level.
As can be observed from these graphs, the current simulation produces identification scores which, in most cases, closely match those achieved by the impaired
subjects.
It can be safely said that it is at least as accurate in predicting the
performance of the impaired subjects as the masking noise employed in the previous
study. The results were judged to be promising enough to warrant the more rigorous
and extensive investigation that is described in the next chapter.
It also appears from these experiments that the present simulation gives at least
as good or somewhat better performance than noise masking. This may indicate
that noise masking has other effects detrimental to intelligibility in addition to
CHAPTER 4. PILOT EVALUATION OF AGC ALGORITHM
elevating thresholds and producing recruitment.
43
CHAPTER 4. PILOT EVALUATION OF AGC ALGORITHM
I
I
I
I
I
-
100
50
-
Flat, 72 dB SPL
U)
-
80
C
C
0
0
130
Impaired
C/
C
0
0-
------ Noise Simulation
1000
HFE, 96dB SPL
C-)
Of
Current Simulation
110
02
50JAA
0 'g
0-
I
-
-20
I
0
20
Q
SNR, dB
Figure 4.7: Consonant identification scores for Group I
44
CHAPTER 4. PILOT EVALUATION OF AGC ALGORITHM
S.
I
F
I
45
I
-
100
Flat, 72 dB SPL
A
Impaired
03
------ Noise Simulation
50C,)
-
Current Simulation
4.
C
0
-I
0100-
I
I
I
-100
Flat, 84 dB SPL.
.
I ID C
0
HFE, 96 dB SPL
El
+I
,' '',t
-'
-
50
'
0
-
50
B
03
0-
-20
I
,I
0
,
,2
20
I1-
Q -20
,
I
0
SNR, dB
Figure 4.8: Consonant identification scores for Group II
K0
20
Q
CHAPTER 4. PILOT EVALUATION OF AGC ALGORITHM
L
I
I
I
,I
i
100-
Flat, 72 dB SPL
13
0
0
El
-
50
C/)
itL
-
0
-
0A Impaired
0
C/)
C
0
-
4-
100
------ Noise Simulation
-
00
HFE, 86 dBL
C)
t
__
o
0
AX
/
-
50
Current Simulation
'
0
B3
0-20
0
20
Q
SNR, dB
Figure 4.9: Consonant identification scores for Group III
46
CHAPTER 4. PILOT EVALUATION OF AGC ALGORITHM
I
I III
I
i
-
100
Flat, 72 dB SPL
-
50
0
C,
a
C:
,-,---..----
4-'
0
Cd
C,)
0
Impaired
0
*O
------ Noise Simulation
0100-
Current Simulation
HFE, 96 dB SPL
0
U'
01
-
-
0.
E
-
50
I'-
-
=
-=
=
00
0
,'6''
0
-20
0
I
20
i
Q
SNR, dB
Figure 4.10: Consonant identification scores for Group IV
47
CHAPTER 4. PILOT EVALUATION OF AGC ALGORITHM
- I
I
I
I
I
48
I.
100-
A
v
o
Flat, 84 dB SPL
_
-
-
Impaired
3
50-
-
------ Noise Simulation
C
(Ti
C:
Current Simulation
o
-
#
Cn
100-
-100
Flat, 94 dB SPL
HFE, 96 dB SPL
0
01
o
--,----
0
-20
--
#--
-0
0
L0
0
20
Q-20
0
20
SNR, dB
Figure 4.11: Consonant identification scores for Group V
Q
Chapter 5
Detailed Evaluation of AGC Algorithm
5.1
Aim
This series of experiments was conducted to provide a rigorous comparison of the
speech intelligibility performance of hearing impaired subjects with that of simulated normals. The goal was to find to what extent the AGC algorithm duplicates
reduced intelligibility and to gain insight into any deficiencies it might have.
The experiments included a consonant identification test similar to that described in the previous chapter as well as sentence comprehension. The stimuli
were presented to three hearing impaired listeners whose losses may be classified as
severe. The magnitudes of these losses were out of the range that can be simulated
with masking noise.
Their performance was compared to that of three normal
hearing subjects listening to the same stimuli passed through the AGC simulation.
Some of the CV tests were repeated using the simulation without including
recruitment. This was done in order to determine the relative importance of
recruitment in determining intelligibility.
5.2
5.2.1
Methods
Speech materials
For the consonant identification experiments the same set of consonant-vowel syllables as used in the pilot study was employed. Their presentation followed exactly
the procedures described in section 4.2.
The sentences were taken from the Harvard-IEEE sentence lists (9).
They
were spoken by a male and recorded in the same anechoic setting as the CVs.
49
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
50
The recorded sentences were low-pass filtered at 4.5 kHz and sampled at 10 kHz.
Their processing and playback followed the same pattern as the CVs (substitute
"Sentences" for "CVs" in Figure 4.2.)
Speech spectrum noise was digitally added to the stimuli as before. Two settings of SNR, the "Quiet" and 0 dB were used in combination with the same two
frequency-gain characteristics (flat and HFE) to yield four stimulus conditions, in
addition to overall level.
5.2.2
Simulation parameters
For the complete simulation all parameters were set to the values given in Chapter 3.
In particular, rather than using a fixed value, the thresholds of recruitment were
computed for each impairment and frequency band. The discomfort thresholds were
not simulated i.e., the simulation provided unity gain for all signal levels above the
recruitment range.
The simulation was altered for the final set of experiments where no recruitment
was simulated. This simply involved changing the transfer characteristic of the gain
computation block in Figure 3.1. The curve above threshold was changed to have
a slope of one. This implied simple and constant attenuation of the signal by the
difference between the impaired threshold and the normal threshold. Section A.2
lists the minor alterations to the code necessary to effect this function.
5.2.3
Subjects
Impaired
The three impaired subjects had previously participated in experiments in the
laboratory and their names were obtained from group files. One of them (PG) had
extensive prior experimental experience. All normally wore a monoaural hearing aid
in the right ear, which was also the ear tested; the aids were not used during testing.
Audiometric records (less than one year old) showed those hearing impairments to be
bilaterally symmetrical and recruiting. Other relevant chracteristics are summarized
in Table 5.1.
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
Subject
PG
AL
Age
49
23
Sex
M
F
PTAt
66
71
Comments
Longstanding
Longstanding
PB
37
F
76
Congenital
t
51
The average hearing loss at 500, 1000 and 2000 Hz in the test ear
Table 5.1: Impaired subject characteristics
Threshold testing was performed in the laboratory. An adaptive forced-choice
psychophysical precedure (10), which targets the 71% correct signal level, was
used to measure thresholds for 500-ms pure tones at octaves from 125 to 4000
Hz (standard audiometric frequencies). The threshold measurement was performed
twice. The average of the two measurements was subsequently used to determine
thresholds in the 14 frequency bands used by the simulation. These thresholds were
determined by linear extrapolation from the measured thresholds. Figure 5.1 shows
the thresholds of the impaired subjects.
Normal
Three normal subjects who had previously participated in psychophysical experiments in the laboratory were recruited. All three were female, ages: KG-44, AW38, DW-41. Their thresholds were measured using the same procedure as for the
impaired subjects. They were found to lie within 15 dB of normal for all frequencies
tested.
None of the subjects (normal or impaired) had listened to Harvard sentences
before this study.
5.2.4
Procedure
CV Syllable tests
The details of the conduct of this test were essentially unchanged from that described in section 4.2.5. The syllable sets were presented in groups of four (four
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
PB
-J 100
0L
I
I
I
Impaired Thresholds
-
I_
-~
AL
Cl)
-PG
~0
.c0
(D
II
52
--
50
FNormal
--
0
.25
1.0
4.0
Frequency, kHz
Figure 5.1: Impaired subject thresholds
conditions for a given overall level). The presentation of successive sets was automatic and the subjects were free to take breaks between sets. For the simulated
normals consecutive sets always simulated the same impairment. In practice that
meant one impairment was simulated per session. A typical session lasted 2 to 3
hours.
Sentence tests
The sentences were presented in groups of 10. They were presented in a setting
much like the syllables. The subjects were prompted on the terminal screen to type
"return" to hear the next sentence. After hearing a sentence they were to write it
down on a pad of paper using underscores for words heard but not understood. After
the first presentation of a sentence and the subject's response the same sentence was
played once more and the subject was asked to identify it again. This was done to
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
53
compensate for the probable advantage of the hearing impaired subjects who would
be more accustomed to the reduced intelligibility of the sentences. Therefore a run
comprised 20 utterances. No feedback was given during or after a run. The scoring
scheme is described in the results section.
5.3
5.3.1
Results
CV Syllables
The performance of the impaired subjects was compared to that of the simulated
normals both in terms of the average percent of consonants identified correctly and
in terms of the pattern of errors, as summarized by an analysis of transmitted
features.
Percent consonants correct
For each subject and each listening condition the experiment was performed at
least seven times (using exactly the same speech tokens); the first two runs were
treated as training and their results were not included in the calculations. It was
observed that the performance of the subjects reached a fairly constant level after
the practice tests. Consequently, each datum represents the average of at least five
experimental outcomes.
Figures 5.2 through 5.6 present the percentages of consonants identified correctly
by the impaired subjects and by the simulated normals for particular stimulus
levels.
These levels were selected individually for the three impaired listeners
to achieve scores well above zero but below 100% correct.
The x-axis lists the
listening condition i.e. combinations of frequency-gain characteristic and signal-tonoise ratio. The dark bar represents the impaired subject while the cross-hatched
bars show the scores of the normals. The error bars indicate the standard deviation
of the scores.
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
54
80
60
0
0
0
40
El
-xi
C
0
0.
IN
PG
DW
KG
AW
20
0
flat, Q
flat, OdB
HFE, Q
HFE, OdB
Condition
Figure 5.2: Consonant identification scores for impairment PG; 94 dB SPL overall
level
80
60
0
AL
C
0 DW
E
E KG
E AW
40
20
0
flat, 0
flat, OdB
HFE, Q
HFE, OdB
Condition
Figure 5.3: Consonant identification scores for impairment AL; 94 dB SPL overall
level
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
55
80
-
60
HAL
40 -DW
0KG
C
~AW
It
20-
0
flat, 0
flat, OdB
HFE, Q
HFE, OdB
Condition
Figure 5.4: Consonant identification scores for impairment AL; 100 dB SPL overall
level
80
60PB
40-
iDW
C
I.
0KG
EAW
20
0
flat, Q
flat, OdB
HFE, Q
HFE, OdB
Condition
Figure 5.5: Consonant identification scores for impairment PB; 98 dB SPL overall
level
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
56
80
60
0W.
H FE,
40,
H
PB
~DW
0. 20
0
flat, Q
flat, 0dB
HEE, Q
HFE, 0dB
Condition
Figure 5.6: Consonant identification scores for impairment PB; 104 dB SPL overall
level
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
57
The data show the following trends. First, for the flat frequency-gain characteristic the matching of scores between the impaireds and the simulated normals is
very close. The notable exception to this pattern is impairment PB at 104 dB SPL
(Figure 5.6) where simulated normals' scores exceed the impaired's score by 5-15
percentage points. Second, the normals perform better under HFE conditions. The
exception to this pattern is PG at 94 dB SPL, where the match is very close. Further,
the discrepancy with HFE increases with increased severity of the impairment (from
PG to AL to PB) and with the level of the stimulus. For subject AL, under the
HFE conditions the normals perform better by an average of 8 percent at an overall
level of 94 dB and 14 percent at 100 dB. For subject PB, the differences were 20
percent at 98 dB and 25 percent at 104 dB.
Feature transmission
Appendix B lists the consonant confusion matrices obtained from the above experiments. Direct comparison of error patterns in such matrices is difficult to perform
particularly with the response bias which was present in some cases. Moreover,
there was an insufficient number of responses per cell to provide reliable estimates
on which to base simulation analyses. Thus we opted for the SINFA analysis (23)
used by many previous investigators.
Table 5.2 lists the features used in the analysis as they apply to the consonants
included in the stimuli.
In the analysis the amount of information available in
each feature for the given stimulus set is computed.
From the confusion matrix
the percent of information in each feature transmitted by the subject is computed.
This was done for each subject for every condition (SNR, gain-characteristic, overall
level) tested. The matrices from individual runs were pooled together prior to this
procedure yielding matrices with each consonant presented at least 15 times.
If the algorithm simulated hearing impairment perfectly, the percent of information transmitted for each feature would be the same for the impaired subject and the
simulated normals. In order to assess the correspondence of feature transmissions a
correlation coefficient, was computed. This coefficient, Cf, was defined as follows:
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
1
+-
+
+
B
D
G
+
1
+
+
1
+
+
1
+r
fricative
TH
S
SH
2
2
2
2
+
+-
+
+
2
3
4
V
XH
-
+
Z
+
ZH
CH
J
M
N
R
+
+
+
+
3
3
4
4
5
5
5
5
5
+
2
2
2
2
+
+
+
+
2
3
4
5
+
1
4
4
1
4
4
5
ling-detal
Y
H
HW
5
2
2
+
1
4
M
M
+
+
IN
R.
5
6
+
+
+
ling-palatal
+
P
M
+
B
P
-+
M
P
B
+
+
+
+
+
ling.velar
+
5
1
+
ling-alveol
+
4
1
+
labi-dental
M
M
+
+
B
P
+
M
M
+
+
B
B
B
P
P
M
B
B
Y
H
B
+
-+
back
DURATION
4-
+
+
4-
41P
T
K
B
D
G
F
TNT
S
SH
V
XH
+
-+
Z
ZN
+
C11
+
+
+
-
middle
+
+
PLACE?3
front
+
+
bilabial
L
+
nasal
sonorant
PLACE-6
W
+
affricate
P
+
K
1
+
T
1
+
stop
JP
+
VOICING
MANNER
+
Peature
58
J
M
W
L
H
Table 5.2: Feature table for Twenty-Four-Consonant set
Cf
=
EM=J~i[M1-
Z
fA)(fn[m]
(f[m]
fi
- j;) 2 Z
-
in)
I(f [m] - f)2
where M is the total number of features, f 1 [m] and fn[m] are the mth feature
transmission percentages for the impaired subject and simulated normal
subject,
respectively, and A, and In are the average feature transmission percentages for the
impaired and normal subjects.
A C1 of 1 indicates complete correlation, i.e. the transmission percentages of
the impaired and normal subject are multiples of each other by a constant ratio. If
plotted versus one another they would lie on a straight line. A coefficient of zero
indicates that the data are uncorrelated.
Figure 5.7 gives an example of a condition for which the correlation between
impaired and normal listeners was 0.84 The symbols plot the percent of information
transmitted by the impaired subject PG for each of the 19 features versus the
analogous score for the simulated normal KG. The listening condition was in quiet,
with flat amplification and overall level of 94 dB SPL. The solid line shows unity
slope. This plot shows a qualitative agreement between feature transmissions typical
of correlations in the 0.80 to 0.90 range.
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
59
100
Flat Quiet, 94 dB SPL
80
EC,,
C:
CI
60I
C
0
00
40I
0
C
0
00
0
20
(5
0
-
00
0
20
0
40
60
80
100
PG % Information Transmitted
Figure 5.7: Example of feature transmission matching; see text for details
Tables 5.3- 5.7 list the correlation coefficients between each impaired subject
and the three simulated normals at all listening conditions.
The feature transmission correlations show generally very good agreement for
all subjects and conditions simulated. The lowest correlation coefficients occur at
one stimulus level for the HFE condition with impaired subject PG (Table 5.3).
This is somewhat surprising since in this case the overall scores of the normals were
closely matched to those of the impaired listener.
5.3.2
Sentences
The performance of the subjects on the sentence comprehension task was evaluated
using a dual approach as well. First, the raw score: percent of keywords identified
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
Listening
Condition
flat, Quiet
flat, 0 dB SNR
HFE, Quiet
HFE, 0 dB SNR
Correlation
DW KG
0.80 0.84
0.85 0.89
0.77 0.55
0.78 0.65
60
(C,)
AW
0.89
0.89
0.50
0.58
Table 5.3: Feature transmission correlation coefficients; impaired subject PG, 94
dB SPL overall level
Listening
Condition
flat, Quiet
flat, 0 dB SNR
HFE, Quiet
HFE, 0 dB SNR
Correlation
DW KG
0.85 0.94
0.92 0.85
0.90 0.89
0.96 0.89
(C,)
AW
0.89
0.82
0.89
0.89
Table 5.4: Feature transmission correlation coefficients; impaired subject AL, 94
dB SPL overall level
Listening
Condition
flat, Quiet
flat, 0 dB SNR
HFE, Quiet
HFE, 0 dB SNR
Correlation
DW KG
0.82 0.87
0.87 0.91
0.94 0.91
0.91 0.90
(C,)
AW
0.84
0.86
0.92
0.85
Table 5.5: Feature transmission correlation coefficients; impaired subject AL, 100
dB SPL overall level
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
Correlation
DW KG
0.90 0.89
0.68 0.85
0.81 0.89
0.80 0.80
(C1
AW
0.87
0.77
0.79
0.77
)
Listening
Condition
flat, Quiet
flat, 0 dB SNR
HFE, Quiet
HFE, 0 dB SNR
61
Table 5.6: Feature transmission correlation coefficients; impaired subject PB, 98
dB SPL overall level
Correlation (C1
DW
KG
0.91
0.91
0.87
0.90
0.76
0.73
0.83
0.87
)
Listening
Condition
flat, Quiet
flat, 0 dB SNR
HFE, Quiet
HFE, 0 dB SNR
Table 5.7: Feature transmission correlation coefficients; impaired subject PB, 104
dB SPL overall level
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
62
Impaired
#
Subjects
PG
AL
per Condition
30
40
flat, quiet
89
94
flat, OdB SNR
94
98
HFE, quiet
89
94
HFE, OdB SNR
89
94
PB
30
104
104
104
104
of Sentences
Overall Level (dB SPL)
Table 5.8: Sentence characteristics by subject and listening condition
correctly, was determined. Second, an attempt was made to determine the degree
to which inter-subject errors were correlated.
Percent Correct
Table 5.8 lists the levels at which the sentences were presented to the impaired
subjects and the number of sentences presented for each listening condition. The
stimuli were then passed through the simulation and presented to the normal
subjects. The normals, therefore, heard the same sentences (albeit processed) as
the impaireds. Different sentences were used for each of the three impaired subjects.
Each of the sentences used in this study contained fie keywords. The subjects'
responses were scored for the number of these keywords they identified correctly.
Mistakes in tense or number were not counted as errors.
The sentences were
presented under the same four listening conditions as the CVs. An attempt was
made to set the overall level of the sentences so that the impaired subjects' scores
were about 50% correct for maximum sensitivity. Only the second response for a
given sentence was used.
Figures 5.8- 5.10 show the overall scores obtained by the impaireds and the
simulated normals. Each score represents the percentage correct out of 30 sentences
containing 150 keywords for impairments PG and PB and 40 sentences containing
200 keywords for impairment AL.
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
1nn
I
800
C.)
60-
U
(I)
0
40-
PG
DW
KG
AW
g
20U
flat, Q
flat, 0 dB
HFE, Q
HFE, 0 dB
Condition
Figure 5.8: Keyword identification for impairment PG
100
*1
20-
["Iii
0
---
80
60
0
40
-
NO
OR
flat, Q
flat, 0 dB
HFE, Q
EU
0
HFE, 0 dB
Condition
Figure 5.9: Keyword identification for impairment AL
AL
DW
KG
AW
63
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
100
-I
-
80
60
-
0
0
I1
E
-
/
40
0
-
20
0-
.1r
flat, Q
flat, 0 dB
HFE, Q
Condition
Figure 5.10: Keyword identification for impairment PB
PB
KG
64
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
65
For the impaired subjects PG and AL the matching of the raw scores was very
good across conditions. The normals appeared to derive a slightly greater benefit for
high-frequency emphasis. Only one normal subject was simulated with the sentences
of impaired listener PB. In this case the normal scored significantly better for all
conditions.
Error patterns
In order to better assess the similarity between the performance of the impaireds
and the simulated normals the following correlation measure was proposed and
implemented. The answers of the impaired subject were compared with those of
the simulated normal, keyword by keyword. Each correct identification was marked
as 1, each incorrect as -1. The sequences thus generated were then multiplied,
point by point and the results summed. This was equivalent to awarding a 1 if the
responses were the same (i.e. both correct or both incorrect) or a -1 for dissimilar
responses (one correct and one incorrect). This was done for all sentences in a given
listening condition. The maximum score for a 30 sentence set would then be 150
and the minimum -150. If the two subjects answered exactly the same way the
maximum score would be obtained'. Tables 5.9 and 5.10 list the correlations thus
computed for impaired subjects PG and AL. The analysis was not performed for
subject PB since her scores were significantly lower than the simulated normal's
and the correlation would have little physical meaning.
To determine the significance of the resulting "keyword correlation" (KC),
probability theory was applied to the product of the two sequences assuming that
they represented independent Bernoulli processes.
The possible outcome of each
process was a 1 or a -1 and the probability of "success" (i.e. a 1) was taken as the
percent of keywords identified correctly by the subject.
The random variable k resulting from multiplying two Bernoulli variables with
probabilities of success P. and P would have a probability distribution
Pk(1) = PaPb
(1 - Pa)(1 - Pb)
'An amusing similarity in confusions occured for subject PG and two normals on the sentence
"The marsh will freeze when cold enough". All three responded with "The Martian __ went home"
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
Listening
Condition
flat, Quiet
flat, 0 dB SNR
HFE, Quiet
HFE,0dB SNR
66
Correlation (KC)
DW KG AW
42
68
74
50
33
46
60
88
58
92
76
92
Table 5.9: Correlation between keyword identification; impaired subject PG, 150
keywords per condition
Listening
Condition
flat, Quiet
flat, 0 dB SNR
HFE, Quiet
HFE,0 dB SNR
Correlation (KC)
DW KG
AW
38
64
45
78
64
70
116 110
104
98
60
99
Table 5.10: Correlation between keyword identification; impaired subject AL, 200
keywords per condition
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
67
Pk(-l) = 1 - Pk(1)
Summing the values of k over all the trials (i.e. keywords) we obtain the correlation
KC under the assumption that the response sequences were uncorrelated (the null
hypothesis). We can easily find the expected value and the standard deviation of
KC under those assumptions:
E(KC) = N(P(1) - Pk(-1))
0
KC
= 4x/K(P(1)P(-1)),
where N is the number of keywords.
By comparing the actual correlation with that calculated under these assumptions we can test the null hypothesis of no correlation. For all cases tested the actual
response correlation between the impaired subject and the simulated normals lay at
least 3 standard deviations above the mean predicted by the null hypothesis. The
hypothesis was thus always rejected at the 0.01 level of significance. We can thus
conclude that the error patterns were significantly correlated.
5.3.3
Simulation without recruitment
As explained above, the normal subjects were also tested with the same sets of
consonant-vowel syllables simulating only the threshold shift of the impaired subject
(i.e. simple filtering).
This was done to investigate the importance of including
recruitment in the simulation. Figures 5.11- 5.13 compare the scores of the impaired
subjects with those of the normals without recruitment. The normal scores represent
an average of three runs.
The simulated normals score consistently better under these conditions than
the impaired subjects. The increase in their scores is particularly evident for the
flat characteristic where their performance improves by an average of 15 percent.
The improvement in the normal subject performance for the HFE condition is less
dramatic although still evident (particularly for impairment PG).
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
68
-
I n80a)
0
4
60-
0
C.)
40a)
0~
E
PG
DW
KG
AW
200
flat, Q
flat, OdB
HFE, Q
HFE, OdB
Condition
Figure 5.11: Consonant identification scores for impairment PG; recruitment not
simulated, 94 dB SPL overall level
I n
I
80-
0
60-
0
Ul
0j
40-
AL
DW
KG
AW
-
20
0
flat, 0
flat, OdB
HFE, 0
HFE, OdB
Condition
Figure 5.12: Consonant identification scores for impairment AL; recruitment not
simulated, 100 dB SPL overall level
CHAPTER 5. DETAILED EVALUATION OF AGC ALGORITHM
179f
i nn
800
60-
C
40-
E3
PB
DW
KG
20-
-
0
flat, 0
flat, OdB
HFE, Q
HFE, OdB
Condition
Figure 5.13: Consonant identification scores for impairment PB; recruitment not
simulated, 104 dB SPL overall level
Chapter 6
Discussion
6.1
Evaluation of performance
The data show several trends reflecting on the accuracy with which the AGC
simulation reproduces the effects of sensorineural hearing impairment on speech
intelligibility.
For virtually all subjects and stimuli, when the flat frequency-gain characteristic
was employed the results obtained from the simulated normals closely match those of
the impaired listeners. This holds true for the consonant reception scores obtained
both in the pilot study and in the experiments of Chapter 5. Collectively these
studies cover a wide range of hearing loss configurations and degrees, from mild
to severe. The scores remain well matched under various signal-to-noise ratios.
Similarly, the raw scores obtained by the impaireds during sentence comprehension
tests were well predicted by the scores of the normals listening to the sentences
passed through the simulation.
Tests designed to investigate the similarities between the patterns of perceptual
errors made by the impaireds and the normals confirmed these results. We evaluated
the perception of consonant features in the CV syllables. In particular, we compared
the percent of information available in each feature that was transmitted to the
listeners. It was shown that the transmission of feature information by the impaireds
was highly correlated with the transmission by the simulated normals. Similarly, the
errors made by the subjects in identifying the keywords in sentences were compared.
Using the "keyword correlation" coefficient described in the previous chapter, it was
shown that the patterns of keyword identification were correlated between subjects
with a high level of significance.
The algorithm performed less well when the high-frequency emphasis character-
70
CHAPTER 6. DISCUSSION
71
istic was employed. Here, the simulated normals tended to perform significantly
better than the impaired subjects. Again, this trend was seen for nearly all stimuli
tested. It was also observed that the discrepancies generally increased with the
severity of the impairment. This is particularly evident for the three impairments
simulated in the foregoing chapter; there are suggestions of similar trends in the
pilot study.
Here, the scores for subject PG (the least severely impaired) were
matched to those of the normals for all conditions including HFE. There were clear
differences for subject AL and yet larger differences for subject PB. For each of the
last two subjects the discrepancies increased with increasing level of the stimulus.
In the pilot study the normals consistently achieved scores above the average of the
impaired subjects' scores under HFE.
The raw scores obtained on the sentence tests showed patterns similar to those
seen with CVs. The discrepancy with HFE was smaller for AL but was quite large
for subject PB (although only one normal was used in that particular simulation).
The results of the error analyses were less conclusive with respect to the difference between flat and HFE conditions. Feature transmission for the impaireds was
about as well matched to the normals under the HFE condition as it was under the
flat condition. One notable exception was subject PG whose feature transmission
was less correlated with that of the normals for HFE than for flat. Likewise, the
error patterns on the keyword identification task generally remained well correlated
under the HFE condition despite the differences in overall performance.
The interpretation we offer for the observed discrepancy with HFE (and the lack
of discrepancy with a flat response) is based on the audibility of speech components
in different frequency regions. From inspection of band levels of the CV stimuli
(24) we expect that for the flat characteristic the stimulus levels were such that
speech-spectrum components above about 2 kHz were below the impaired subjects' thresholds. The HFE filter raised those components above threshold while
attenuating the low frequencies.
In broad terms this pattern of results indicates
the presence of significant high frequency suprathreshold effects in sensorineural
hearing impairment that are not represented by the simulation. Such effects are
not present when the speech signal exceeds the impaired listener's threshold only
CHAPTER 6. DISCUSSION
72
at low frequencies (below about 2 kHz).
-
Greater upward spread of masking in impaired ears can be offered as an explanation for the observed discrepancy with HFE. The direction of the masking effect
greater masking of high frequency signals by low-frequency maskers than the reverse
- is consistent with the direction of the observed discrepancy between simulated
and impaired performance being greater with HFE than with flat responses. Since
the simulation does not explicitly account for masking phenomena the simulated
normals are not affected while the impaireds are. One would expect this effect to be
particularly evident when the high frequency components are above threshold (in
quiet) but are accompanied by low-frequency components. For the flat frequency
response high-frequency components are less likely to be above quiet threshold, so
that high-frequency information is unavailable to both the simulated-normal and
the impaired listeners.
The discrepancy with the HFE condition might not be as dramatic for sentences
because the impaireds are better practiced to utilize the contextual information.
The normals who are not used to the quality of speech in the simulation might
require a longer training period.
The correlation scores are harder to integrate into this rationalization since they
appeared almost uniformly good for all conditions. As pointed out above the one
place they do fail is in fact for the HFE condition. At this time I do not have a
good explanation for this result although it appears to be consistent with results
reported elsewhere (3; 22) which show lack of correlation between raw scores and
feature error patterns.
6.2
Importance of recruitment
The experiments conducted with the recruitment-less simulation seem to unequivocally indicate that recruitment plays some role in diminishing the intelligibility of
speech for the hearing impaired. The normal subjects scored better when recruitment was absent than when it was present. This was true of virtually all conditions
for which this test was performed. Thus, while the elevated threshold still appears
CHAPTER 6. DISCUSSION
73
to play a paramount role in determining the effect of a hearing impairment the
phenomenon of recruitment also has to be accounted for in the simulation.
6.3
Conclusions
The simulation proposed in this thesis was largely successful in duplicating the
speech reception of the hearing impaired for normal listeners. The experiments
conducted indicate that the simulation faithfully reproduces intelligibility scores for
speech reproduced with a flat frequency response. At those frequencies it appears
that threshold shift and loudness recruitment as simulated by the AGC algorithm
collectively determine the intelligibility of speech as perceived by impaired subjects.
For stimuli with high-frequency emphasis other suprathreshold effects appear to
dgrade intelligibility for the impaired listeners. The effect proposed to be the factor
is upward spread of masking. The algorithm in its present form does not attempt to
simulate this effect. However, the structure of the algorithm may be conceptually
extended to account for this phenomenon. In Figure 3.1 the gain computation for
each channel is affected only by the signal level in that particular frequency band.
It is possible to simulate more complex interactions by allowing the gain in one
channel to be influenced by the signal level in other channels. Future work in this
direction should consider such ways of conditioning the gain.
While the experiments conducted during this study yield fairly consistent results
it would be instructive to obtain a better evaluation of the qualitative fidelity of
the simulation. An ideal experiment would have a unilaterally impaired subject
compare the sensation in the impaired ear with that in the normal ear listening to
the simulation. Due to limited contact with patients I was unable to recruit such a
subject for this study. If possible in the future such an experiment is likely to yield
invaluable additional insight.
Bibliography
[1] Diane K. Bustamante. Principal Component Amplitude Compression of Speech
for the Hearing Impaired, Ph.D. Thesis, Massachusetts Institute of Technology,
February 1986.
[21 William F. Carver.
Loudness Balance Procedures,
in Handbook of Clinical
Audiology. Jack Katz, ed., The Williams & Wilkins Co., Baltimore, 1978.
[3] David A. Fabry and Dianne J. Van Tasell. Masked and Filtered Simulation of
Hearing Loss: Effects on Consonant Recognition, Journalof Speech and Hearing
Research, 29: 170-178, 1986.
[4] Jean-Pierre Gagne and Norman P. Erber. Simulation of Sensorineural Hearing
Impairment. Ear and Hearing, 8(4):232-243, 1987.
[5] Daniel W. Griffin and Jae S. Lim. Signal Estimation from Modified ShortTime Fourier Transform. IEEE Transactions on Acoustics, Speech and Signal
Processing, 32(2):236-243, April 1984.
[6] C.S. Hallpike and J.D. Hood. Observations Upon the Neurological Mechanism
of the Loudness Recruitment Phenomenon. Acta Oto-laryngologica, 50:472-486,
1959.
[7] Rhona P. Hellman and Carol H. Meiselman Prediction of Individual Loudness
Exponents From Cross-modality Matching Journalof Speech and Hearing Re-
search, 31, 1988
[8] Larry E. Humes. Spectral and Temporal Resolution by the Hearing Impaired.
in The Vanderbilt Hearing Aid Report, Gerald A. Studebaker and Fred H. Bess,
eds., Monographs in Contemporary Audiology, Upper Darby PA, 1982.
[9] IEEE. IEEE Recommended Practicefor Speech Quality Measurements. Techni-
cal Report IEEE 297, Institute of Electrical and Electronics Engineers, 1969.
74
BIBLIOGRAPHY
75
[10] Harry Levitt.
/it The
Transformed up-down methods in psychoacoustics.
Journal of the Acoustical Society of America, 49:467-477, 1971.
[11] Harry Levitt. Speech Discrimination Ability in the Hearing Impaired: Spectrum Considerations. in The Vanderbilt Hearing Aid Report, Gerald A. Stude-
baker and Fred H. Bess, eds., Monographs in Contemporary Audiology, Upper
Darby PA, 1982.
[12] F. Miskolczy-Fodor. Relation between Loudness and Duration of Tonal Pulses.
III. Response in Cases of Abnormal Loudness Function. The Journal of the
Acoustical Society of America, 32(4):486-492, 1960.
[13] Patrick M. Peterson and Joseph A. Frisbie. An interactive environment for
signal processing on a VAX computer. Proceedings of ICA SSP, 1891-1894, April
1987.
[14] D.P. Phillips. Stimulus intensity and loudness recruitment: Neural correlates.
The Journal of the Acoustical Society of America, 82(1):1-12, 1987.
[15] R.W. Schafer and L. R. Rabiner. Design of Digital Filter Banks for Speech
Analysis. The Bell System Technical Journal, 50(10):3097-3115, 1971
[16] Bertram Scharf and Mary Florentine. Psychoacoustics of Elementary Sounds.
in The Vanderbilt Hearing Aid Report, Gerald A. Studebaker and Fred H. Bess,
eds., Monographs in Contemporary Audiology, Upper Darby PA, 1982.
[17] Margaret W. Skinner.
Speech intelligibility in noise-induced hearing loss:
Effects of high-frequency compensation. The Journal of the Acoustical Society
of America, 67(1):306-317, 1980.
[18] S.S. Stevens. Power-Group Transformations under Glare, Masking, and Recruitment.
The Journal of the Acoustical Society of America, 39(4):725-735,
1966.
[19] S.S. Stevens and Miguelina Guirao. Loudness functions under inhibition Perception & Psychophysics, 2(10):459-465, 1967.
BIBLIOGRAPHY
76
[20] Edgar Villchur. Simulation of the effect of recruitment on loudness relationships in speech. The Journal of the Acoustical Society of America, 56(5):1601-
1611, 1974.
[21] Edgar Villchur. Electronic models to simulate the effect of sensory distortions
on speech perception by the deaf.
The Journal of the Acoustical Society of
America, 62(3):665-674, 1977.
[22] Brian E. Walden, Daniel M. Schwartz, Allen A. Montgomery and Robert A.
Prosek. A Comparison of the Effects of Hearing Impairment and Acoustic Filtering on Consonant Recognition. The Journal of Speech and Hearing Research,
46: 32-43, 1981.
[23] Marilyn D. Wang and Robert C. Bilger. Consonants confusions in noise: a
study of perceptual features. The Journal of the Acoustical Society of America,
54(5):1248-1266, 1973.
[24] Patrick M. Zurek and Lorraine A. Delhorne.
Consonant reception in noise
by listeners with mild and moderate sensorineural hearing impairment.
Journal of the Acoustical Society of America, 82(5):1548 -1559, 1987.
The
Appendix A
Simulation Program
A.1
Full code
This is the program used to implement the AGC simulation described in Chapter
3. It was written in VAX C programming language for the VAX VMS environment
on a VAX 11/750 computer.
It is essentially self-contained except for some I/O
routines which were written in the laboratory.
The AGC simulation program
/* Uses the BNIO I/0 package written by Patrick Peterson.
/* All binary input assumed to have the CBG format one block header.
*/
/* Arguments: 1) input signal file name
/*
2) impaired thresholds file name
3) overall output level in dB SPL
4) output signal file name
5) RMS of the input signal
/* The following directory is to be defined externally as a logical
*/
/* name: fildir: location of bandpass filters; filter impulse responses */
/* stored under f"x".s where "x" is the number of the filter.
/*
/*
/*
/*
/*
/*
Input and output signals are stored as short integers.
Filters are stored as double floats.
The threshold file is ASCII list of three rows listing for all 14
frequency bands: 1) impaired threshold, 2) threshold of impairment,
3) impaired threshold of discomfort. This version of the program
computes the recruitment thresholds therefore line 2 is ignored
#include
#include
#define
#define
#define
#define
#define
#define
#define
#define
#define
stdio
math
STMAI
BYTBLK
SHRBLK
INTBLK
FLTBLK
WINDL
DBOUT
NUMCHAN
LN10
100000
512
256
128
128
/*
20
-200.0
14
2.302585093
window length in milliseconds */
77
*/
*/
*/
*/
*/
*/
APPENDIX A. SIMULATION PROGRAM
#define PI
3.141592 654
#define TINY
le-32
#define FILTDELAY 113
/*
78
offset in filterbank to give zero delay */
#define max(A, B)
#define min(A, B)
((A) > (B) ? (A)
((A) < (B) ? (A)
#define cadd(z, x, y)
#define csub(z, X, y)
#define cmul(z, x, y)
z.real = x.real + y.real; z.imag = x.imag + y.imag
z.real = x.real
(B))
(B))
-
y.real; z.imag = x.imag - y.imag
z.real = x.real * y.real - x.imag * y.imag;\
z.imag = x.real * y.imag + x.imag * y.real
/* Normal (ANSI) thresholds in dB SPL */
double nthlist[NUMCHAN] = { 45., 35., 23., 16., 11., 10.,
10., 10., 10., 10. };
struct
9., 7., 8., 9.,
cmplx{
double
double
}
real;
imag;
cxaddo, cxsubo, cxmul();
static
int
dummy = 0;
main(argc, argv)
int
argc;
char
{
*argv[];
char
char
int
int
int
short
float
double
double
double
struct
FILE
filtname[100], thfile[100], fout[100];
thlist[512], *thlistptr;
i, j, fn, siglen, filtlen, mf, ms, wlen;
filthead[INTBLK], buf[INTBLK];
floattocmplxo, inttocmplxo, padlen;
sig[STMAI], icvout[STMAI];
filt[8096];
env[STMAIJ, expenv[STMAX], fcvout[STMAI], chan[STMAI];
rmsout, impth, recth, dcmth, normth, normdth;
recompo, gettho, atof();
cmplx
filtcx[STMAI], sigcx[STMAI;
*fopen(;
for(j = 0;
j<
STMAI; *(fcvout +
j++)
= 0)
/*
initialize sum
/* read threshold recruitment threshold and discomfort threshold file
fn = opentargv[2], 0);
thlistptr = thlist;
while ((j = read(fn, thlistptr++, 1)) > 0);
close(fn);
/* read the input signal
fn = openbn(vmstring(argv[1]), &1);
getbn(&fn, buf, k(BYTBLK));
*/
/* read header */
APPENDIX A. SIMULATION PROGRAM
79
siglen = buf[0];
wlen = (1000 * WINDL) / buf[1];
for (i = 0; i <= (siglen / SHRBLK); i++)
/* read signal */
getbn(kfn, Big + i * SHRBLK, k(BYTBLK));
closbn(kfn);
/*
read filter for channel 1
mkfilt(filtname, 1);
fn = openbn(vmstring(filtname), 1W);
getbn(fn, filthead, A(BYTBLK));
filtlen = filthead[O];
closbn(kfn);
*/
/* read header */
/* find FFT of input signal
ms = inttocmplx (sig. siglen, (filtlen + siglen), sigcx);
padlen = pwr(2, ms);
fft (sigcx, ms);
/* Loop to process all channels
for(j = 1; j <= NUMCHAN;
/*
j++){
*/
read filter, find its FFT
mkfilt(filtname, j);
fn = openbn(vmstring(filtname), 14);
getbn(kfn, filthead, k(BYTBLK));
/* read header */
for (i = 0; i <= (filtlen / FLTBLK); i++)
getbn(&fn, filt + i * FLTBLK, k(BYTBLK));
closbn(kfn);
mf = floattocmplx (filt, filtlen, padlen, filtcx);
fft (filtcx, mf);
/* multiply signal and filter FFTs, find channel output
for (i = 0; i < padlen; i++)
filtcx[i] = cxmul(filtcx[i], sigcx[i]);
ifft(filtcx, mf);
for (i = 0; i < siglen; i++)
chan[i] = filtcx[i + FILTDELAY].real;
/* get thresholds: impaired, threshold of recruitment, threshold of
/* discomfort, normal and normal discomfort, adjust for external gain
rmsout = 20 * log10( atof(argv[5]));
impth = getth(thlist, j, 1) - atof(argv[3]) + rmsout;
recth = recomp(getth(thlist, j, 1), *(nthlist+j-1))
- atof(argv[3]) + rmsout;
dcmth = getth(thlist, j, 3) - atof(argv[3]) + rmsout;
normth = *(nthlist+j-1) - atof(argv[3]) + rmsout;
normdth = 120. - atof(argv[3]) + rmsout;
/* find channel level, gain and processed channel output, add to total
log-rms-env(chan, env, siglen, wlen);
*/
*/
APPENDIX A. SIMULATION PROGRAM
80
envexpand(env, expenv, impth, recth, dcmth, normth,
normdth, siglen);
chanout(chan, fcvout, env, expenv, siglen);
}
for(i = 0; i < siglen; i++)
*(icvout+i) = *(fcvout+i);
/*
/* convert to int */
write the simulation output
fn = creabn(vmstring(argv[4]), &512, &0);
putbn(fn, buf, &(BYTBLK));
/* write header */
for (i = 0; i <= (siglen / SHRBLK); i++)
/* write file
*/
putbn(kfn, icvout + i * SHRBLK, k(BYTBLK));
closbn(kfn);
/* subroutine to find the name of current filter file
mkfilt (filtname, chnum)
char
*filtname;
int
chnum;
char
char
char
chord[100], *strcato;
pcl[100] = "fildir:f";
pc2[100] = ".s";
*filtname = "\0
sprintf (chord, "*d", chnum);
strcpy(filtname, strcat(pcl, strcat(chord, pc2)));
/* subroutine to get nth element out of the line of threshold list
/* indicated by code
double getth(tlist, n, code)
char
*tlist;
int
n, code;
int
double
i, j;
atof();
for(j = 1; j < (code - 1) * NUMCHAN + n;
for(; *tlist=='
I *tlist=='\n'
for(; *tlist >= '0'
&k
j++){
11 *tlist=='\t'; tlist++)
*tlist <= '9'
11 *tlist== '.;
tlist++)
return(atof(tlist));
/* subroutine to compute the level waveform of the channel
logrms-env(sig, env, siglen, wlen)
APPENDIX A. SIMULATION PROGRAM
short
double
{
81
*sig, siglen, wlen;
*env;
int
double
i;
avs = 0.;
for(i = 0; i <= wlen/2; i++, sig++)
avs = avs + (double)(*sig * *sig) / wlen;
= 10. * loglO(avs);
for(i = wlen/2+1; i < ulen; i++, sig++){
avs = avs + (double)(*sig * *sig) / wlen;
*(env++) = 10. * log1O(avs);
}
for(i = wlen; i < siglen; i++, sig++){
avs = avs + (double)
(*sig * *sig - *(sig-wlen) * *(sig-wlen))
/
*(env++)
*(env++) = 10. * loglO(avs);
}
for(i = wlen/2; i > 0; i--){
avs = avs - (double)(*(sig-i) * *(sig-i))
*(env++) = 10. * loglO(avs);
/ wlen;
}
}
/* subroutine to expand the envelope according to the recruitment
/* function for gain computation
envexpand(env, expenv, ith, rth, dth, nth, ndth, siglen)
double *env, *expenv, ith, rth, dth, nth, ndth;
int
siglen;
{
int
double
i;
lth, pth, eli, s12, s13, icti, ict2, ict3;
lth = ith - 5.0;
pth = dth - 5.0;
/*lower thresh for rise to thresh*/
/*thresh for rise to discomfort */
eli = (nth s12 = (rth s13 = (ndth icti = DBOUT
ict2 = nth -
DBOUT) / 5.0;
/* slope for
nth) / (rth - ith);/* slope for
pth) / 5.0;
/* slope for
- sli * lth;
/* intercept
rise up to thresh
recruitment section
rise to discomfort
for rise to thresh
/* intercept for recruit curve
ict3 = pth -
s13 * pth;
s12 * ith;
*/
*/
*/
*/
*/
/* intercept, rise to discomfort */
for(i = 0; i < siglen; i++)
if (*(env+i) <= lth)
*(expenv+i) = DBOUT;
else if (*(env+i) <= ith)
*(expenv+i) = icti + eli * *(env+i);
else if (*(env+i) <= rth)
*(expenv+i) = ict2 + s12 * *(env+i);
else if (*(env+i) <= pth)
*(expenv+i) = *(env+i);
else if (*(env+i) <= dth)
APPENDIX A. SIMULATION PROGRAM
else
82
*(expenv+i) = ict3 + s13 * *(env+i);
*(expenv+i) = *(env+i) + ndth - dth;
}
/* subroutine to compute gain for the channel,
/* and maintain a running output total
scale the channel input */
*/
chan-out(sigin, sumout, env, expenv, siglen)
int
siglen;
double
*sigin, *sumout, *env, *expenv;
{
int
i;
*(sumout+i) +=
*(sigin+i)
* exp( LN10
(*(expenv+i)
*
for(i = 0; i < siglen; i++)
-
*(env+i))
/* subroutine to compute the treshold of recruitment
double recomp(impth, normth)
double
/ 20.);
*/
impth, normth;
{
double
recangle, slope;
recangle = (PI / 180.) * (47. + 0.45 * (impth - normth));
slope = tan(recangle);
return(min(max(((slope * (impth - normth)) / (slope
(impth + 15.)), (impth + 40.)));
}
/* conversion of float array to array of cmplx structures
int
floattocmplx (arr, arrlen, maxlen, arrcx)
float
*arr;
arrlen, maxlen
cmplx
arrcx[l;
int
struct
{
int
i,
j, padlen;
i = 0;
while ((padlen = pwr(2,i++)) < maxlen);
for (j = 0; j < arrlen; j++){
arrcx[j].real = *(arr + j;
arrcx[j].imag = 0;
}
for
(j = arrlen; j < padlen; j++)
arrcx[j].real = arrcx[j].imag = 0;
return(i-1);
/* conversion of integer array to array of cmplx
int
inttocmplx (arr, arrlen, maxlen, arrcx)
*arr;
short
structures
-
1) + normth),
APPENDIX A. SIMULATION PROGRAM
int
struct
{
83
arrlen, maxlen
cmplx
arrcx[j;
int
i = 0;
i,
j,
padlen;
while ((padlen = pwr(2,i++)) < maxlen);
for (j = 0; j < arrlen; j++){
arrcx[j].real = *(arr + j);
arrcx[j].imag = 0;
}
for (j = arrlen; j < padlen; j++)
arrcx[j].real = arrcx[j].imag = 0;
return(i-1);
/* decimation in time FFT
fft (x, m)
struct cmplx
x[];
int
{
m;
struct
struct
int
cmplx
cxaddo, cxsubo), cxmul();
cmplx
wn[STMAX/2], ti, t2;
i, j, k, len, gp, gph, gpnum;
len = pwr(2,m);
for (i = 0 i < len / 2; i++){
wnli].real = cos((double) (2 * i * (PI / len)))
wn[i].imag = -sin((double) (2 * i * (PI / len));
}
for (i = 0; i < m-1; i++){
printf("%d ",i);
gpnum = pwr(2,i);
gp = pwr(2, m) / pwr(2, i);
gph = gp / 2;
for (j = 0; j < gpnum; j++){
for (k = 0; k < gph; k++){
cadd(ti, x[j*gp+k], x[j*gp+gph+k]);
csub(t2, x[j*gp+k], x[j*gp+gph+k]);
cmul(x[j*gp+gph+k], t2, wn[k*gpnum]);
x[j*gp+k] = ti;
}
}
}
for (i = 0; i < len; i += 2){
ti = cxadd(x[i] x[i+1]);
x[i+1] = cxsub(x[i], x[i+1]);
x[i] = ti;
}
}
*/
APPENDIX A. SIMULATION PROGRAM
/* decimation in frequency IFFT
ifft (x, m)
struct cmplx
x[];
int
m;
{
struct
struct
int
cmplx
cxaddo, cxsubo, cxmul();
cmplx
wn[STMAX/2], ti, t2;
i, j, k, len, gp, gph, gpnium;
len = pwr(2,m);
for (i = 0 i < len / 2; i++){
wn[i].real = cos((double) (2 * i * (PI / len)));
wn[i].imag = sin((double) (2 * i * (PI / len)));
}
for (i = 0; i < len; i += 2){
ti = cxadd(x[i], x[i+1]);
x[i+1] = cxsub(x[i], x[i+1]);
x[i] = ti;
}
for (i = 1; i < m; i++){
gpnum = pwr(2, m-(1+i));
gp = pwr(2, m) / gpnum;
gph = gp / 2;
for (j = 0; j < gpnum; j++){
for (k = 0; k < gph; k++){
cmul(tl, x[j*gp+gph+k], wn[k*gpnum]);
cadd(t2, x[j*gp+k], ti);
csub(x[j*gp+gph+k], x[j*gp+k], ti);
x[j*gp+k] = t2;
}
}
}
for (i = 0; i < len; x[i].real /= len, i++);
/* miscellaneous functions: complex add, subtract and multiply
struct cmplx
cxadd(x, y)
struct cmplx
x, y;
{
struct
cmplx
z;
z.real = x.real + y.real;
z.imag = x.imag + y.imag;
return(z);
struct
struct
{
cmplx
cmplx
cxsub(x, y)
x, y;
struct
cmplx
z;
84
APPENDIX A. SIMULATION PROGRAM
85
z.real = x.real - y.real;
z.imag = x.imag - y.imag;
return(z);
}
struct
struct
{
cmplx
cmplx
cxmul(x,
x, y;
struct
cmplx
y)
z;
z.real = x.real * y.real - x.imag * y.imag;
z.imag = x.real * y.imag + x.imag * y.real;
return(z);
}
/* integer exponentiation function
int
pwr(base, expo)
int
base, expo;
{
int
a;
a = 1;
if (expo < O){
fprintf(stderr, "pwr: exponent out of range\n");
exit(1);
}
else{
for (; expo > 0; a *= base, expo--);
return (a);
}
A.2
}
Program changes for no recruitment
The main alteration necessary to simulate normal growth of loudness above threshold was the repalcement of the env-expando) routine as shown below. In addition,
the input had to be scaled up digitally in order to avoid serious quantization problems (the simulated stimuli had much lower amplitude than the original
utterances).
The scale factor was input as the sixth argument. It was compensated for by
appropriate adjustment of the external analog gain.
\* Altered level expansion for no recruitment
envexpand(env, expenv, ith, nth, siglen)
double *env, *expenv, ith, nth;
int
siglen;
APPENDIX A. SIMULATION PROGRAM
{
int
double
i;
lth, sli, icti;
lth = ith
-
sli = (nth
5.0;
/* love r thresh for smooth rise*/
/* to threshold
- DBOUT) / 5.0;
icti = DBOUT - sli * lth;
/* slope for rise up to thresh */
/* inte rcept, rise to thresh
*/
for(i = 0; i < siglen; i++)
if (*(env+i) <= lth)
*(expenv+i) = DBOUT;
else if (*(env+i) <= ith)
else
I
*(expenv+i) = icti + sli * *(env+i);
*(expenv+i) = *(env+i) - (ith - nth);
86
Appendix B
Consonant Confusion Matrices
The following are the consonant confusion matrices obtained from the CV identification tests described in Chapter 5. All the simulated normal responses have been
pooled together to render one matrix for each impairment and listening condition.
The impaired subject data for each listening condition appear at the top of each page
and the simulated normal data for that impairment and condition at the bottom.
The heading of the impaired subject matrix identifies the impairment, overall level
of the stimuli (in dB SPL), the frequency-gain characteristic and the signal-to-noise
ratio.
87
APPENDIX B. CONSONANT CONFUSION MATRICES
1
17
-
-
-
-1
-
1
-
-
1
7
-
--
2
-
3
6
3
4
6
1
-
-
-
3
2
11
-
-
1
2
-
16
-
-
-
1
-
-
-
12
-
7
40
33
16
-
1
-S
25
17
-
-
-
-
4
-
-
-
-
-
4
-
-
-
2
-
1
-
17
12
13
13
-
3
3
-
20
11
-
-
4
-
-
-
43
-
-
2
5
7
16
-
-
17
10
-
4
-
7
11
3
-
3
--17
-
2
2
1
N
-
-
8
3
-
-
-
-
2
-
18
7
29 189
62
-
4
19
22
41
1
-
3
6
60 106
N
1
1
3
4
2
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1
-
12
3
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18
-
-
-
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1
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1
J
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CH
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1
2
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16
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34
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1
-
-
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4
R
22
-
-
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29
1
-
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2
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13
21
69
63
63
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WE
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2
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8
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4
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2
-
5
2
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-
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V
L
-
-
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-
-
1
-
-
-
-
-
-
-
-
1
-
-
-
2
-
2
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V
-
SH
32
432
1080 Presentations
- -
-
11
-
-
6
- - -
-
S
-
-
TH
-
-
F
-
-
G
-
-
4
-
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1
9
3
-
-
-
2
-
-
-
-
106
-
-
-
-
7
3
32
-
-
-
34
-
-
27
-
1
1
1
-
-
1
2
19
-
-
40
9
64
66
64
34
42
1
45
45
45
4
10
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2
2
45
5
4
45
45
45
45
-
-
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-
17
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1
-
9
-
39
-1
- -
15
-
1
-
28
-
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2
1
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-
17
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1
-
-
-
-
-
-
-
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1
-
-
-
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2
1
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1
3
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6
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33
7
10
37
25
13
2
1
-
10
-
-
6
2
--
1
-
D
-
4
-
-1
-
40.5 Percent Correct out of
B
5
-4
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2
-
17
I
18
-
-
-
1
-
-
6
-
-
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VB
-
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V
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y
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16
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1
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CH
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13
1
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-
26
-
P
T
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B
D
G
F
TH
S
SH
V
IN
-
-
22
-
-
Three simulated normals
T
-
11
-
-
-
P
2
1
2
-
3
24
-
-
-
R
V
L
y
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11
1
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1
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
-
-
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2
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5
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1
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3
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1
12
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-
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6
1
4
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2
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1
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3
V
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F
-
5
1
12
G
-
13
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D
-
6
1
2
B
-
I
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T
432 Presentations
-
P
T
I
B
D
G
F
TH
S
SH
V
IN
Z
ZH
CH
J
P
47.5 Percent Correct out of
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Quiet
Impairment PG, 94 dB, flat,
88
6
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
45
1080
APPENDIX B. CONSONANT CONFUSION MATRICES
-
1
1
3
-
1
-
1
1
-
-
-
2
-
15
25
25
2
4
37
-
2
4
3
1
18
25
49
13
2
3
-
-
4
-
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3
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1
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17
38
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B
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T
6
6
2
8
2
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1
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4
2
25
1
B
4
1
D
-
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-
G
2
2
4
2
2
1
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CH
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13
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3
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if
R
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1
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1
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36
50
8
13
13
14
6
-
27
-
20
3
2
-
-
3
4
-
2
4
6
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2
1
1
1
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1
1
2
8
3
10
1
-
1
3
12
18
14
12
15
V
-
6
-
-
-
-
-
7
I
-
4
2
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4
4
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3
IN
Z
2
-
-
-
-
-
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1
1
1
2
2
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8
-
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2
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1
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5
2
-
-
1
-
-
-
5
-
1
-
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2
30
35
-
-
2
-
4
-
19
18
-
-
-
5
-
119
-
-
-
3
2
-
2
-
4
55
57 56
40
-
3
-
-
6
1
10
-
-
1
-
-
12
5
45
92
39
19
44
35
-
-
-
2
3
3
1
-
-
2
4
12
12
1
-
-1
-1
11
4
2
-
-
-
3
26
R
S
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1
N
10
1
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N
CH
-
5
-1
1
1
1
3
J
ZE
-
1
1
-
-
-
5
-
27
31
11
432
1080 Presentations
-
6
3
1
-
2
8
-
1
-
3
SH
43
-
-
3
1
8
1
1
1
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-
23 162
-
1
-
2
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10
-
9
2
1
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2
2
1
7
2
S
-
15
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TH
-
1
9
1
5
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1
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y
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WN
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F
-
-
-
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G
-
F
TH
S
SH
V
IN
-
-
K
-
T
4
15
1
34.5 Percent Correct out of
P
-
-
1
-
16
-
2
2
Three simulated normals
16
-
2
-
-
-
-
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2
5
1
-
1
20
-
V
L
Y
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WE
-
3
3
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2
2
4
3
2
1
1
5
-
-
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1
-
-
1
1
9
35
-
28
2
3
3
4
24
-
22
92
48
1
3
-
1
-
-
-
-
-
5
6
1
5
6
-
-
2
-
6
3
3
-
1
3
4
-
5
36
4
4
5
13
-
1
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
45
45
45
45
45
45
45
45
45
45
45
45
1
1
45
-
-
2
-
-
45
- - -
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2
1
1
-
-
-
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45
2
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6
2
1
-
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1
13
45
45
45
45
45
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45
45
45
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45
55
46
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3
WE
-
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H
Y
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1
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4
3
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6
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2
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3
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1
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-
1
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6
1
2
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1
1
1
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-
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1
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2
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1
12
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-
-
-
2
9
-
S
-
2
1
2
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1
TH
-
-
5
F
-
G
432 Presentations
31.7 Percent Correct out of
-
D
-
B
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R
V
L
y
N
WE
I
-
P
T
K
B
D
G
F
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CH
J
T
0 dB SIR
-
P
PG, 94 dB, flat,
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Impairment
89
39
1080
90
APPENDIX B. CONSONANT CONFUSION MATRICES
-
21
4
10
23
z
ZH
CH
-
- - - -45
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1
-
-
-
4
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6
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3
-
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-
-
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17
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5
34
6
29
----
6
-
1
1
2
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11
2
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5
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17
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12
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43
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3
IN
-
-
-
-
N
I
-
-
-
-
R
-
-
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V
L
-
-
-
-
3
-
-
-
-
-
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-
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1
-
-
-
-
-
-
-
-
-
-
-
-
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12
-
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I
-
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24
47
37
88
47
60
69
22
12
46
2
27
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ZN
CH
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2
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14
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6
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33
5
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15
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-
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1
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12
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11
30
19
13
13
13
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42
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37
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30
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22
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Y
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V
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45
45
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1
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3
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1
45
45
45
45
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45
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8
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-
-
-
46
1
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8
45
45
45
45
45
-
-
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1
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37
- 33
-
-
-
-
-
16
45
45
33
25
1080
3
45
-
5
-
45
-
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-
-
-
84
-
-
-
1
-
-
45
--
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-
-102
-38
- -
8
61
432
1080 Presentations
2
4
6
1
45
-
J.
y
H
WE
1
-
- -
9
2
8
-
-
3
2
-
21
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3
3
29
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V
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SH
-
1
1
1
12
2
1
13
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S
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TH
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SH
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-
B
D
G
7
-
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8
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F
19
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12
3
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58.6 Percent Correct out of
G
15
1
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1
-
-
48
D
31
9
-
-
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B
16
-
-
-
2
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-
P
T
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T
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1
Three simulated normals
P
16
-
-
-
18
-
1
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4
3
-
2
-
4
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27
-
-
18
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26
4
8
1
1
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21
-
-
-
10
1
-
20
4
-
-
4
-
-
11
-
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
-
-
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1
1
2------------1
2-
WV
-
-
R
v
L
y
H
WH
-
-
-
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8
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-
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1
1
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1
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2
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R
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2
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5
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14
12
9
CH
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IN
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1
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V
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-
3
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4
18
S
-
-
-
13
-
TH
-
-
-
-
-
F
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-
5
16
G
432 Presentations
-
17
-
D
-
-
-
-
B
- -
9
I
- - -
P
T
I
B
D
G
F
TH
S
SH
V
IN
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ZN
CH
J
N
I
T
58.1 Percent Correct out of
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P
HFE, Quiet
-
Impairment PG, 94 dB,
45
45
-
-
-
-
-
29
-
-
30
54
29
20
66
46
67
APPENDIX B. CONSONANT CONFUSION MATRICES
-
-
-
2
-
2
-
-
-
-
1
-
-
-
-
1
1
-
-
-
1
3
-
-
5
1
9
27
-
5
32
5
19
K
B
D
G
F
TH
S
SH
V
IN
Z
ZI
24
-
31
17
8
13
12
-
-
2
35
2
3
19
8
3
16
27
-
-
-
-
-
-
-
-
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9
-
-
-
-
2
11
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-
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1
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TH
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Z
ZH
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2
2
12
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3
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1
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12
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15
5
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1
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2
1
1
1
15
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1
6
1
1
5
19
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-
1
-
42
-
18
5
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6
-
-
-
1
4
27
-
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2
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3
5
1
36
1
29
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-
1
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-
-
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-
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2
10
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y
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WV
1
-
1
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0
70
R
W
-
33
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-
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6335
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-
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-
6
1
63
-
-
-
5
-
-
-
-
-
-
1
40
34
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5
1
81
9
1
-
12
-
14
21
12
15
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-
1
29
10
24
11
17
52.3 Percent Correct out of
T
B
D
G
6
-
-6
1
-
4
12
15
-
13
-
-
-
-
-
-
-
-
-
-
-
7
-
-
-
28
23
49
I
55
CH
13
11
-
K
-
-
1
P
-
P
T
8
2-
-
18
Three simulated normals
----5
1
3
-
J
45
31
-
-
-
R
V
L
Y
H WE
-
2
1
1----------------3
2
2
-
8
3
-
1
5
-
45
45
45
45
45
45
-
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1
8 -- -
4
-
-
-
-
1
-
7
-
-
-
-
-
---
-
-1
-
-
-
-
-
-
-
5
-
1
-
-
-
1
-
-
-
-
-
35 100
432
1080 Presentations
I
N
3
-
40
5
-
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
-
22
-
2
2
--
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1
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---
-
24
3
13
-
20
--
-
-
31
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14
16
12
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-
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-
-
-
-
-
14
-
3
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1
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1
1
-
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1
-
1-
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2
-
-
-
1
--
-
-
-
3
1
4
1
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-
5
-
45
45
45
45
45
45
45
1
-
-
-
45
45
45
45
45
-
29
-
23
3
45
2
-
20
-
2
-
10
45
45
45
50 41
30
56
45
61
43
19
1080
-
26
-
45
45
45
-
-
-
-
2
-
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-
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1
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-
1
-
3
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-
1
-
3
-
-
-
-
-
-
-
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-
-
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-
2
4
1
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3
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-
1
7
6
13
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4
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6
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Y
-
-
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-
6
10
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V
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-
1
R
-
-
-
1
9
I
-
-
-
-
K
-
-
13
-
J
-
-
18
2
-
-
-
-
2
2
2
-
-
CH
-
1
1
-
1
-
-
-
ZE
- -
-
-
-
Z
-
1
-
-
-
10
-
IN
-
-
16
V
-
-
-
2
SH
-
-
17
-
S
-
7
-
TH
-
F
-
G
-
D
- -
2
B
-
5
I
-
P
T
K
B
D
G
F
TH
S
SH
V
IN
z
ZI
CH
J
N
N
R
W
L
y
H
WE
T
-
P
432 Presentations
51.9 Percent Correct out of
-
Impairment PG, 94 dB, IFE, 0 dB SIR
91
5
APPENDIX B. CONSONANT CONFUSION MATRICES
Impairment AL, 94 dB, flat, Quiet
-
24
7
7
-
-
26
-
15
2
-
1-
2
-
-
-
-
1
-
-
-
3
1
-
-
43
12
-
-
-
-
5
6
-
-
-
1
1
--
-
3S
61
-
-
0
-
-
46
1
9
-
-
17
-
-
-
-
26
-
-
45
10
32
-
5
-
-
-
-
-
-
-
-
-
-
-
4
-
-
-
F
TH
S
SH
V
S
-
-
-
5
-
5
10
1
25
13
-
-
-
15
-
2
-
-
-
-
6
7--
-
2
-
4-5--
14 116 29 107
-
-
-
5
-
-
--
-
73
-
1 11
1
-
3---------
-
6
2
-
5
-
-
-
-
-
-
-
4
-
-
-
-
-1
-
-
1
-
-
-
-
2
5
-
4-
3
3
2
-
-
14
3
2
1
-
9
2
3
20
5
-15
4
7
12
648
-
-
2
-24
-
-
-
-
-
-
42
3
--
-
-
-
-
-
----
-
-
6
-
-
-
-
22
--
-
-
25
13
22
4
18
49
2
-
6
0
IN
2
10
1
-
1
5
-
9
5-
3
53
-
-
-
-
Z
ZH
CH
J
7
-
21
4
1
23
2
-
26
25
N
-
-
1
6
2
2
9
29
38
-
20
62
1296 Presentations
I
R
V
L
Y
I
WE
1
3
1
-
6
1
-
-
-
-
1-
-
-
3
-
2
-
1
12
-
-
-
-
8
2
1
1
-
-
---
--
-
-
-
-
-
-
-
-
2
-
1
-
-
4
17
-
-
34
-
20
-
-
-
-
-
-
-
-
-
-
-
-
-
10
-
45
-
32
-
10
34
-
1
-
- 50
-
-
I
-
10
84
14
-
35
88
31
-
-
17
1
10
14
-
-
-
6
4
63
-
98
1
1
1
-
36
7
21
-
-
-
4
1
-11
-
7
-
-
2
4
28
2
8
7
2
10
29
-
26
2
-
11
78
85
60
34
1
3
5
-
9
-----
58
-
-
3
-
-
-
1----- -----11
51
-
-
-
-
2
-
-
I--1
46
-
18
10
-
-
53
-4
-
-
2
-1
-
20
25
-
G
10
-
-
D
-
4
- -
2
-
-
3
3
-
-
-
-
-
-
- -
19
49
25
-1
-
4
-
-
50.0 Percent Correct out of
B
K
-
-
P
T
K
B
D
G
F
TH
S
SH
V
IN
Z
ZH
CH
J
N
I
R
W
L
y
H
WE
T
-
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
-1
22
-
-
-
1
Three simulated normals
P
S
-
-
-
-
-
-1
26
-
-
-
-
-
53
-
-
-
-
2
-
26
1
-
-
-
-
-
-I
-
-
-
wi
-
-
1
2
I
-
-
2
Y
-
-
1
L
-
-
1
V
- -
-
R
53
-
-
1
-1
I
- -
--
K
18
- -
--
-
J
- -
2
-
CH
- -
13
-
-
1
-
-
1
-
-
ZN
-
1
3
-
1
Z
-
-
16
-
-
IN
-
-
-
-
-
5
3
-
V
-
W
L
y
N
WE
-
-
7
27
1
-
-
XIH
ZE
ZN
CH
J
N
1
-
-
1
-
-
1
10
1
- -
-
-
-
-
1
-
-
SH
-
9
-
S
-
1
7
-
TH
- -
1
-
F
-
1
3
17
G
648 Presentations
-
5
27
7
11
-
-
P
T
K
B
D
G
F
TH
S
SH
D
-
B
-
K
54.8 Percent Correct out of
-
T
P
92
9
49
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
1296
APPENDIX B. CONSONANT CONFUSION MATRICES
-
13
1
-
13
-
-
-
-
-
-
6
3
2
5
-
1
-
-
1
5
-
2
10
1
-
8
-
-
-
7
-
41
11
93
0
1
-
51
3
2
-
1
1
2
7
13
8
-
-
-
25
-
-
3
1
-
-
9
-
-
-
40
2
1
-
16
-
-
1-
1
-
-
2
-
2
31
17
8
47
19
15
12
-
7
4
-
-I
-
7
IN
Z
ZN
CH
J
N
-
--
7
-
1
-
--
--
1
-
--
7
-
3
-
-
-
-
6
-
48
-
17
-
13
3
-
-
-
-
-
-
1
-
-
-
-
-
5
-
1
-
4
-
1
1
6
-
1
-
-
4
-
-
22
121
18
97
76
19
62
2
46 52
-
-
-
-
-
2
-
3
2
3
4
-
53
-
-
--
--
-
6
4
-
23
49
22 - 35
-
19
4
-
1
9
16
60
27
-
-
1
-
-
-9
-
-
-
92
-
-
-
-
4
-
6
-
-
-
4
-
-
-
3
7
4
7
-
-
29
5
31
18
N
R
W
L
Y
2
2
2
-
2
-
N
WE
-
10
2
2
-
7
3
-
-
-
1
1
-
2
13
11
-
12
-
-
1
1
3
3
-
3
-
V
-
-
-
-
648
1296 Presentations
--
SH
-
3
1
86
-
-
11
16
-
-
-4
24
20
-
I
-
10
6
-
11
8
-
-
4
-
4
14
-
-
-
-
--
-
-
-
--
8
1
-
3
-
-
7
8
14
26
-
-
1
-
-
22
4
25
1
-
7
5
-
-
2
11
91
31
70
3
1
54
54
54
54
54
54
7
3
-
1
1
-
1
-
-
2
8
1
5
1
-
1
6
8
-
-
-
-
1
6
5
2
2
2
1
-
6
7
-
-
-1
7
54
54
54
54
54
54
54
-
6
2
I
8
-
-
4
17
2
1
1
-
-
-
33
35
-
6
-
- - -
11
2
S
-
12
TH
-
14
-
33
-
-
-
-
F
-
G
-
D
-
B
-
ZN
CH
J
N
N
R
W
L
y
H
WE
I
2
2
-
z
T
45
25
16
42.7 Percent Correct out of
P
3
-
-
7
-
2
-
23
1
7
5
-
1
2
-
56
-
-
6
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
1
-
1
-
-
- - -
-
3
5
3
Three simulated normals
P
T
I
B
D
G
F
TH
S
SH
V
IN
1
-
-1
1
5
-
2
2
20
-
-
-
-
1
-
-
-
1
1
2
-
-
-
5
2
14
25
1
2
3
16
34
13
76
-
514
-
-
2
-
47
-
14
-
99
63
55
23
1
28
-
-
-
27
-
H WE
3
13
54
54
54
54
54
54
54
-
-
Y
54
-
8
-
-
L
-
-1
12
-
-
-
1
V
-
1
- -
9
-
N
-
1
N
-
-2
W
L
y
N
WE
9
7
1
J
-
3
-
CH
-
-
-
ZN
-
-
3
Z
-
3
-
-
1
8
-
IN
- -
1
4-
2
-
- - -
-
10
V
-
-
-
1
1
8
-
-
-
-
1
1
-
-
-
-
4
SH
27
-
S
-
16
-1
-
R
-
-
TH
-
2
F
-
8
27
8
G
-
2
-
D
-
P
T
I
B
D
G
F
TH
S
SH
V
IN
Z
ZH
CH
J
H
I
B
-
I
-
T
648 Presentations
39.5 Percent Correct out of
-
P
0 dB SIR
-
94 dB, flat,
-
AL,
Impairment
93
54
6
54
17
54
56
1296
APPENDIX B. CONSONANT CONFUSION MATRICES
AL,
94 dB,
P
T
I
18
9
26
-
-
-
-
3
3
4
9
4
-
-
-
-
2
1
-
17
67
16
1
1
-
7
7
80
1
-
--
-
- ---59
-
-
31
-
-
-
14
R
-
-
3 310
19 1
-
-
4
-
-
2
--
24-
53
1
-
-
-
-6
-
-
-
-
2
-
-
9
-
-
7
95
35
86
S
2
3
-
-
-
41
88
38
6
61
1
-
-
-
y
H
WE
-
-2
46
-
-
-
13
-
24
10
-2
-5
2
-
2
-
2
66
-
-1
23
-
-
-
2
8
-
6
-
-
4
1
-
3
-
7
21
---
2
15
i
--
17
29
37
-
-
19
52
7
-1
2
7
-
2
3
1
13
7
3
1
2
16
8
20
11
30
--
-1
-2
-
271-
-
19
--
1
-3
7
648
1296 Presentations
-
CH
J
N
I
R
V
L
Y
N
-
-
-
-
-
-
-
3-
-
-
-
-
7
-
-
-
-
-
-
-
-
-
-
54
-
-
-
76
- - 3
54
54
-
-
-
-
-
-
-
5
118
1
4-
1
-
3-
-
-
-
-
-
-
-
-
-
-
I
19
-
1
1
-
14
-
-
36
14
27
3
-
3
24
4
-
-
9
3
37
-
-10 10
-51
-
-
-
-
-
13
-
2
1
59
-
ZN
1
-
55
-
Z
-
2
-
IN
-
- -
-
V
L
-
-
- -
N
N1
-
37
ZN
CH
2
V
-
8
z
J
SH
1
-
14
S
-
-
-
3
1
28
-
TH
-
5
-
1
F
-
16
54
G
-
D
19
-
1
2
-
1
-
27
-
-
10
1
1
--
9
1
1
-
-
27
-
1
-
4
-
7
-
-
6
60.0 Percent Correct out of
-
B
1
-
-
-
-
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
- -
21
1
-
2
-
-
1
-
-
-
-
-
-
-
-
-
-
17
27
-
-
-
-
-
1
-
1
-
-
--
1
-
2
2
-
4
-
2
-
13'
-
92
35
54-2923
-
-
43
-
94
-
1
-
53
4
2
56
1
-
1
35
6
21
31
32
63
5
68
WE
-
5
7
1
6
-
-
-
23
-
64
37
50
-
54
54
54
54
54
-
-2
-
-
H WE
Y
54
-
2
-
F
TH
S
SH
V
IN
L
- - -
9
-
2
-
V
-
-
19
49
10
-
R
1
54
54
54
54
54
54
54
54
- -
2-
26
7
I
54
12
54
54
- -
-1
I
7
-
N
--
-
-
--
1
2
-
2
-
8
-
-
-
-
17
-
T
B
D
G
J
-
- -
19
4
8
271
-
1
-
-
-
I
CN
-
-
12
-
P
1
1
ZE
- --
-
14
Three simulated normals
P
T
Z
-
56
IN
-
21
1
V
-
2
-
N
R
L
y
H
WE
-
-
1
SH
-
1
-
-
S
648 Presentations
1
8
3
-
-
8
3
19
27
9
-
-
-
TH
19
7
-
1
F
G
-
8
D
49.1 Percent Correct out of
-
-
B
Quiet
-
P
T
I
B
D
G
F
TN
S
SH
V
IH
Z
ZH
CH
HFE,
-
Impairment
94
54
54
1
10
54
24
1296
54
APPENDIX B. CONSONANT CONFUSION MATRICES
-
3
1
-
-
-
-
-
7
1
-
-
-
2
-
-
-
-
-
1
ZH
CH
-
-
3
-
1
1
-
-
-
-
1
-3
-
-
-
1
-
2
-
-
-
1
24
17
-
1
1
2
2
- - -
-
27
2
-
-
7
2
-
-
3
2
--
--
2
-
-
-
2
4-
9
13
-
1
27
-3
-
3
11
2
1
-
40
15
28
0
46
31
1
G
F
4
2
-
-
-
-
-
1
-
-
1
4
49
4
2
-
-
4
-
9
-
14
-
-
-
31
2
-
-
-
-
-
I
1
-
1
-22
22
6-4
-
-
-
-
-
88
-
-
6
25 75
6
-
77
S
SH
V
IN
Z
4
1
3
2
1
-
8
-
-
15
-
-
11
-
5
10
5
-
4
2
1
-
1
2
8
3
9
1
25
2
1
2
18
-
7
42
98
16
14
-1
-
44
N
3
13
-1
1
4
-
-
-
-
-
-
3
-
1
-
-
10
3
-
-
18
-
-
-
-
-
-
-
-
-
-
-
-
28
-
1
-
-
5
I
-
47
13
3
7
-
2
12
3
-
-
-
-
7
-
7
-
58 52
19
-I
1
I
67
-
-
54-
8
-
-
-
-
3
-
-
-
1
-
-
-
-
-4
1
-
-
-
10
48
1
-
-
6
10
-
1
53
27
-
- -
65
-
Y
3-
53
33
-
27 103
-5
6
24
17
-
1
-
63
-
-
I
12
22
12
11
2
7
2
6
648
1296 Presentations
L
1--
5
20
V
-
14
32
R
-
- -
12
I
-
-
8
K
-
--
2
3
J
-
-
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
9
CH
ZN
-
-
1
3
-
35
-
3
5
V
-
4
-
N
N1
5
-
TH
-
1
20
10
y
H
WE
D
16
50
59
-
14
B
13
-
22
7
-
I
10
-
T
0
45
53.4 Percent Correct out of
-- - -
P
-
L
-
-
-
-
1
-
4
-
-
-
-
-
-
3
-
-
14
31
-
13
-
1
-
6
7
24
10
30
75
57
26
68
1
6
1
3
2
3
16
-
-
-
-
-
1
-
8
5
WE
54
54
54
54
54
54
1
1
54
54
54
54
54
54
54
54
54
3
-
8
-
-
22
--
R
-
-
-
-
18
-
3
H WE
2
47
-
24
-
- -
61
-
F
TH
S
SE
V
IN
z
ZE
CH
Y
-
Three simulated normals
B
D
G
4
2
3
14
K
L
25
y
N
WE
P
T
1
-1
-
L
-
-
--
-
- --
1
1
-
7
4
-
J
m
I
R
v
-
1
-
V
R
- - -
6
3
-
I
-
1
9
-
N
J
-
5
-
CH
- - -
-
2
ZN
14
54
54
54
54
69
51
59
35
1296
5
5
31
-
1
1
- - - - - -
2
26
Z
-
11
-
IN
V
-
1
SH
-
-
S
-
12
4
1
TH
-
7
25
14
7
2
F
-
10
2
1
1
G
-
D
- -
B
648 Presentations
Correct out of
-
Z
K
45.2 Percent
--
TH
S
SH
v
IN
T
-
B
D
G
F
P
-
I
0 dB Sfl
-
P
T
HFE,
-
Impairment AL, 94 dB,
95
1
17
1
54
54
54
54
54
V
IN
Z
ZH
CH
J
N
2
6
12
-
-
4
18
2
-
9
-
12
-
-
-
-
-
-
-
-
-
-
6
18
-
9
3
-
-
-
13
-
-
-
-
-
-
-
-
-
-
18
11
-
3
-
- - -
--1
- --- -4-- -----------2
- -------
1
-
-
4
-
-
-
8
-
-
-
-
6
18
-
-
1
-
2
2
-
2
-
- -
-
17
27
18
23
24
16
-
17
2
3
9
2
3
-
-
-
-
10
7
19
24
15
32
16
1
51
0
23
19
20
0
13
9
29
10
20
16
4
-
-
-
17
-
-
-
16
-
35
-
-
-
1
1
3
37
-
48
24
22
4
-
-
-
-
26
-
17
3
1
1
-
-
-
1
-
2
-
-
-1
N
-
2
-
-1
R
-
-
-
-
-
-
3
-
-
-
31
78
10
1
-
5
-
1
-
-
1
7
32
37
2
-
-
1
-
-
-
1
-
3
-
7
33
-
-
-
-
-
-
-
4
-
2
IR
Z
ZH
- -
3
-
V
1
-
-
-
I
-
-
-
21
-
- I-
-
-
2
-
-
-
- - - -
3
SH
- - -
S
52
7
1
2
8
-
TH
18
-
F
7
-
G
-
y
H
V
1
62.9 Percent Correct out of
ZR
CH
V
L
-
-
D
2
1
-4
-
6-
-
-51
2
-
42
-
CH
J
-
-
53
20
6
-
-
-
-
-
11
-
-
3
1
-
-
8
1
45 122
79
16
35
52
59
1-
-
8
18
18
18
18
18
18
3
3
432
I
R
V
L
Y
R
VI
1
-
-
-
-
-
-
1
-
-
-
-
-
-
-
-
-
-
-
-
-
-2
-
-
-
--
-
-
--
-
2
9
1
31
-
-
-
-
-
-
54
-
-
54
54
54
2
54
-
24
-
73
55
44
-
-
-
-
-
-
-
-
75
45
76
43
-
4
18
4
3
1296 Presentations
K
-
-
-
-
-
1
2
43
-
23
11
I
21
64
50
34
69
40
14
29 100
S
S
-
-
z
-
-1
-
-
B
-
-
-
-
-
IN
ZR
4
1
-
-
I
I
1
-
-
- -
2-- ----16
F
TH
S
SH
V
-
-
2
-
D
G
-
-
T
52
-
-
-
I
B
24
-
-
-
P
T
-
-
Three simulated normals
P
-
-6
11
3
9
-
3
43
1
7
11
3
6
43
1
54
54
-
-
18
-
-
112-
- -
-
-
3
-
-
-
--
-
-
-
-
54
54
54
54
54
54
54
-
-
-
-
-
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
- - -
-
-I
--
-
-
V
N
- -
-
14
Y
1
S
- -
2
-
L
-
5
V
-
18
R
I
-
SH
- -
S
- - -
TH
- -
F
- -
G
-
-
D
- - -
-
B
-
5
-
I
- -
10
1
-
T
432 Presentations
- - -
P
T
K
B
D
G
F
TH
S
SH
V
IN
Z
ZR
CH
J
x
I
R
V
L
y
H
VK
62.5 Percent Correct out of
- -
P
96
-
Quiet
Impairment AL, 100 dB, flat,
-
-
APPENDIX B. CONSONANT CONFUSION MATRICES
6
14
54
54
54
54
54
54
54
54
54
54
54
1296
APPENDIX B. CONSONANT CONFUSION MATRICES
--
-1
--
13
-
-
2
-
--
-
R
-
-
4
L
y
HI
-
30
13
20
--
-
-
-
-
-
-
-
-
-
-
-
2-3
-
- -
-
3
25
12
42
0
24
17
24
L
y
H
WE
-
3
21
2
27
10
-
-
I
3
27
2
-
-
-
-
-
-
-
-
-
-
-
1
--
-
-
-
-
-
I
-
S
SH
-
19
3
-
-
-
45
8
19 16
-
-
-
-
-
23
21
3
-
-
2
-
-
1 12
- 16
1
-
-
-
1
5
9
-
-
-
38
-
-
-
54
-
-
1
-
3
-
-
-
-
-
-
-
-
-
-
10
-
-
6
-
-
3
-
-
-
-
-
-
6
3
3
-
-
-
-
-
TH
-
-
-
4-----------------
-
-
V
3
-
1
-
--
2
1
-
-
1
-
-
2
-
1
1
-1
-
-
-
4
-
3
-
15
-
-
-
-
-
--
1
-
10
-
-
-
-12
-
1
9
5
2
-
-
14
6
14
3
8
27
1
1
53
2
1
-
-
-
- -
1
1
27
19
13
12
V
L
Y
N
WE
-
-
-
-
-
1
-
-
-
4
2--
--
--
2-
--
3
3
--2
3--------------1
- 12-
--
1
1
-
6-
--
1 1
-
3
-
-
-
-
--
3
11
10
1
-
-
7
-
-
2
1
-
-
11
-
8
-
-
2
-
-
-
-
-
2
27
87
32
95
16
R
-
4
-
-
1---
-
-
-
-
-
-
3
-
41
-
10
27
-
53
-
53
26
-
8
-
26 119
I
-
1
39
54
1296 Presentations
N
-
-
-
-
3
-
55
8
68
41
80
-
38
1 3
35
S1134
1
1 4
1
3
82
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
432
J
ZH
5
25
CH
Z
5
70
-
-
13
IN
-
3-
-
- -
53
- 15
3
-
3
-
3
-
F
- -
18
G
- -
D
-
B
14
-
0
-
I
-
-4
-
N
R
T
-
10
13
57.4 Percent Correct out of
Three simulated normals
P
T
K
B
D
G
F
TH
S
SH
V
IN
Z
ZH
CH
-
-
-2
2-1
--
-
-
-
-
2
4
-
2
39
-
2-
10
-
6
1
-
WE
- --
21
-
-
4-
-8
-
12
1
12I1
-1
1
8
-
-
-
54
5
7
28
4
3
27
2
-
28
-
37
86
58
60
68
1
30
1
-
6
39
1
1
1
5
-
-4
-1
4
- -
1
1
1
4
-
-
-
- - - - - -
-
2
-
P
-
-
- -
-5
-
-
WE
-
-
18--
-
K
-
-
-
1 17-
-4
6
-
-
Y
-
-
13
5
L
--
-
-
V
-
3
1
2
-
-
R
-
-
-
-
I
11
-
-
-
-
-
-
-
1
-
-
-
-
-
-
-
-
I
2
N
- -
-
-
5
-
J
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
10
54
54
- -
-
1
3
CH
-
-1
-
-
ZN
-
-
-
6
-
Z
-
2
17
-
-
-
9
-
-
IN
-
4
V
-
1
2
SH
- - -
-
-
S
-
-
-
11
TH
-
1
F
-
3
-
G
-
1-
D
-
18
7
- -
-
-
B
432 Presentations
-
za
10
-
-
P
T
I
B
D
G
F
TH
S
SH
V
IN
zZN
CH
K
T
52.1 Percent Correct out of
0 dB SIR
-
P
AL, 100 dB, flat,
-
Impairment
97
54
54
2
10
54
31
1296
64
APPENDIX B. CONSONANT CONFUSION MATRICES
Impairment AL, 100 dB, NFE, Quiet
SH
-
-
15
-
-
2
1
18
1 17
-
10
14
9
4
3
9-
9
-
73
0
2
34
28
26
-
-
-
-
-
-
-
-
-
2
2
--21 1
V
L
y
H
WE
45
63
--
-
2
1
-
45
55
87
1
18
16
5
-
2
12
1
3
40
8
18
12
6
18
-
-
-
1
14
-
-
-
-
-
-
-
-
-
-
-
-
-
3
-
7
-
-
-
-
-
-
-
2
-
-
-
1
2
4
-
3
7
9
-
-
1----------5
7
-
1
2
-
7
-
-
-
-
-
-
--
--
--
--
-
-
33
-
42
-
-
-
45
3
-
-
24
513
-
1 11
-I
-
-
--
-
--
8
1-
16
8
-
-
-
13
-
37 107
17
--
-
7
58 54
38
-
N
R
V
L
Y
4
H
9
-
V
-
--
1296 Presentations
H
2I
-
432
11
J
-
-
17
CH
SH
-18
10
ZN
S
6
-
4
52
1
-
-3
R
1
16
-
-
-51
-
14
-
- - -
-2
-
18
54
9
-
1
30
-
11
Z
TH
--
5
2
-
-
-I
IN
F
-
-
-
1
-
-1
-
-
15
3
- - -
-
-
-
-
35
-
8
1
G
-
6
-
2
53
D
-
1
-
-
67.4 Percent Correct out of
-
1
B
-
--
N
34
I
T
-
1
-
Three simulated normals
P
6
1
-
-
13
76
45
1
1
83
54
54
-
-54
-2625
-
50
wi
-
39
-I
51
4
-
54
14
-
-
1
8
-
39
22
2
3
1
51
-
54
62
-
-
-
-
54
54
-
14
4
- - -
17
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
54
54
54
54
54
5
54
54
54
54
54
40
1- 3
-
30
3
62
45
33
45
12
5
2
-
31
-
83
62
54
-
-
25
-
-
-
12
H WE
Y
- -
1
-
1
-6
-
L
54
54
54
54
12
54
- -
-
2
-
2
-1
-3
-1
-
V
-
V
L
y
H
WH
4
3
-
1
-
1
-
R
-
13
I
1
-
2
H
-
-
J
-
6
CH
- - -
18
6
ZH
-
10
-
Z
-
8
-
R
P
T
K
B
D
G
F
TH
S
SN
V
IN
Z
ZH
CH
IH
V
-
S
-
TH
-
1
-
F
432 Presentations
-
6
G
-
1
17
-
D
52.8 Percent Correct out of
-
12
-
B
-
T
-
P
T
K
B
D
G
F
TH
S
SH
V
IN
Z
ZH
CH
J
H
N
P
98
54
54
54
1
3
16
54
1296
APPENDIX B. CONSONANT CONFUSION MATRICES
2
3
39
4
2
0
32
27
38
-
-
-
-
12
3
-
TH
S
SH
-
-
-
----
-
-
-
-
-
IH
z
-
ZH
CH
-
52
12
1 7
---26
-
-
-
-
--
-
-
-
-
-
-
-
L
y
-
-
112
-
-
-
H
WE
3
-
1
-
12
-
63
40
82
73
R
-
-
43
-
-
V
IN
-
-
-
9
-
-
33
-
2
11
-
-
-
-
2
36
13
-
11
-
42
2
-
8
2
6
-
-
-
12
-
2
4
-
2
-
-
-
-
-
N
SH
-
-
1
-
-
-
13
-
-
15----------
-
I
-
-
-
-
-
9
2
-
9
-
-
37
1
-
-
-1
-
3
-
17
1
11
-
11
32
13
20
-
1
89
-
-
-
-
54
-
-------
5
-
-
4 11
-
-
-
-
Z
42
7
7
11
-
V
L
Y
K
-
-
-
-
-
-
-
-
-
-
-
1
-
-
-
-
-
-
-
-
-10
-
-
-
-
-
-
-
6
-
-
7
-
1
-
-
3
I
1
-
-
-
2
-
-
-
-
4
-
-
1
-
5
2
-
-
1
-
-
-
1
1
1
-
1
-
2
-
4
-
2
-
11
50
1
-
-
-
7
-
-
-
-
3
-
-
-
-
26
44
-
58
-
4
66
WE
R
-
15
64
-
51
28
-
-
-
2
45
-
-
-
-
-
8
1
21
-
-
-
-
-
49
91
1
-
611
39
9
1
1
432
1296 Presentations
N
4
6
1
K
2
ZH
38
J
-
-
1
CH
-
-
-
-
2
21
2
1
S
1
2
3
2
3
- -
7
1
-
TH
- - -
F
- - -
G
-
1
1
0
-
D
1
1
-
-
B
D
G
F
I
B
-
1
10
62.7 Percent Correct out of
I
29
3
-
11
- -
1
1
-
44
2
-
-
25
-
1
-
-
2
1
3
45
65
66
30
5
-
1
-
38
2
78
35
-
-
5
-
-
1
-
52
1
4
3
- -
30
1
7
1
-
2
-
P
T
T
-
-
-
-
1
-
10
Three simulated normals
P
18
16
-
-
12
-
-2
- -
23
-
-
-
-
12
-
-
-1
-
20
-
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
- -
13
-
-
29
-
WE
-
-
6
-
1
4
38
1
-
-
12
5
5
67
-
23
-
61
30
54
- - -
-
44
12
-
I
- -
L
y
NK
-
Y
-
4
-
-
L
-
1
-
V
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
2
54
54
54
54
14
1296
- -
1
-
v
R
R
-
-
-
9
5
3
-
3
-
1
I
2
1 17
3
- -
ZN
CH
I
-
z
3
4
N
11
6
-
2
-
-
J
-
1
CH
- -
-
1
ZN
-
1
8
- - -
6
1
Z
-
S
18
-
IN
-
2
V
-
1
S
3
-
1
4
-
16
-
-
SH
-
3
S
-
-
TH
-
3
F
6
3
-
G
-
1
D
-
1
13
B
432 Presentations
-
6
2
-
I
-
T
-
P
T
I
B
D
G
F
TH
S
SH
V
IN
P
45.1 Percent Correct out of
- -
Impairment AL, 100 dB, NFE, 0 dB SIR
99
APPENDIX B. CONSONANT CONFUSION MATRICES
56
36
2
-
3
2
1
1
-
4
-
---
14
1
-
-
4
4
2
33
1
38
47
1
2
-
9
34
-1
CH
2
N
-
-
2
39
23
15
29
1
4
7
8
3
4
-
1
1
1
1
-
-
-
8
8
2
1
-
8
2
3
-
1
-
1
-
CH
J
N
-
-
-
1
-
1
2
-
14
2
-
3
-
1
4
3
-
-
-
5
1
-
-
-
-
-
32
3
3
-
1
9-
6-
-
10
1
-
11
4
-
-
-
-
1
52
-
2
- 11
-10
-
-
5
7
1
1
-
1
-
-
-
-
-
-
-
2
-
3
3
1
-
ZN
-
-7
-
22-
7 1
6---
1
-
11---
1
19
2 45
1
- 28 18
----
----
-
1
-
-
41
9
18
6
14
-
I
2
-
12
2
Z
2
7
8
6
34
-
7
-
4
1
54 16 148
44
14
67
14
6
-
84-
53 127
17
-
-
3
22
-
-
3
5
89 S2 113
-
1
1
4
-
10
-
-
4
7
2
4
3
12
2
1
12
2
22
5
1
7
1
26
68
32
62
9
648
1296 Presentations
IN
7I
7
-
5
R W
L
Y
H
1
-
9
-
1
1
-
2
54
4
-
-
-
-
7
-
S
-
1
54
54
-
2
-
3
10
3
-
12
3
-
-
1
1
1
7
4
WE
8
7
2
6
54
54
54
-
3
54
54
5
1
3
54
54
54
54
-
-
-
V
2
-
1
1
4
27
3
5
-
y
H
WE
2
1
-
L
-
-
1
-
12
2
-
R
W
-
5
2
20
1
6
-
-S
1
-
1
-
3
1
3I
-
-
ZN
33
10
3
-
-
IH
ZE
-
-
9
SN
-
3
13
17
11
9
5
7
-
-
1
2
6
-
1
-
-
5
1
-
23
F
-
-
-
1
TH
S
G
-
1
9
1
-
6
D
-
2
14
-
16
-
32.6 Percent Correct out of
-
4
22
10
-1
3
2
10
-
-
-
F
TH
S
SH
13
1
4
-
D
G
B
-
K
B
I
-
P
T
T
-1
-
3
16
1
Three simulated normals
P
-
4
4
1
2
4
2
1
6
7
-
3
-
8
-
1
-
-
3
14
2
4
7
-
-
-
3
13
49
11
- - -
9
1
4
6
2
-1
-
1
1
-
-
2
-
2
3
3
-
1
1
-
28
-
-
-
3
4
1
-
- 22-
-
1
-
1
-
2
21
16
-
3
-
1
1
4
3
41
5
-
4
1
2
1
2
-
-
-
2
1
-
1
-
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
1
57
54
54
54
7
3
8
1
5
18
2
-
3
-
-
8
22
2
8 15
6
27
84
5
82
1
-
87
-
-
3
-
-
-
WE
-
5
-
K
- -
2
7
1
1
1
1
-
4
1
-
-I
-
-
-
-
-
6
2-I
-
-
9
1
4
-
-
-
1
6
-
1
1
4
-
-
-
-
-
-
3
2
2
2
-
3
-
-4
-8
-
2
Y
-
21
-
-
S
8
-
-
L
-
-
5
-
-
V
-
2
2
R
-
3
1
I
-
S
2
-
1
N
-
-
6
-
2
5
1
J
-
-
-
4
1
-
CH
-
-
2
2
2
2
ZN
-
-
-
5
Z
-
-
3
3
IN
648 Presentations
-
-
-
V
-
1
-
-
1
12
2
SH
-
7
-
S
-
2
S
-
-
-
TH
-
1
3
F
-
2
10
G
-
9
D
-
-1
-
1
-
-
-
1
1
-
1
B
-
R
W
L
y
N
WE
2
11
2
I
-
ZH
CH
1
1
-
z
T
-
P
T
I
B
D
G
F
TH
S
SH
V
IN
P
29.2 Percent Correct out of
-
Impairment PB, 98 dB, flat, Quiet
100
1
54
54
54
54
54
54
64
-
9
54
54
49
47
1296
APPENDIX B. CONSONANT CONFUSION MATRICES
62
27
38
36
11
-
2
-
18
5
-
-
11
4
-
13
-
-
10
10
-
-
8
2
8
-
-
7
4
7
6
-
4
3
-
-
1
-
3
S
-
4
-
-
9
3
-
10
4
3
-
-
-
6
2
-
-
5
10
2
1
2
4
-
5
11 167
72
12
86
1
1
-
5
4
1
-
39
78
-
-
-
1
-
5
3
10
31
3
1
38
-
14
7
7
-
-
3
-
-
4
1
-
-
-
-
-
-
-
13
2
4
S5
15
4
-
3
-
11
3-
Z
2
-
-
I
-
8
4
IN
-
-
-
-
-
2
12
18
14
2
-
9
10
-
2
-
1-
14-2
4
6
2
1
16
23
25
3
-
22
2
4
-
1
1
2
2
26
CH
--
6
-
-
3
-
6
-
1
2-
9
42 126
ZN
5
-
-
-
1
1
-
2
-
5
1
3
2
-
4
-
3
1
4
35
33
77
-
-
-
-
1
2
6
2
1
1
4
-
2
5
-
8
29
20
13
648
1296 Presentations
-
6
-
1
1
45
-
-
2
33
-6
25
-
11
-
37
3
1
-
-
33
-
-
-
-
-
4
7
2
-
1
3
2
I
1
-
4
5
5
5
1
N
-
2
3
1
I
1
J
9
1
-6
3
-
-
1
R
V
L
Y
-
1
1
7
-
-
V
-
-
SN
-
1
8
1
-
2
4
-
-
1
7
13
-29
9
-
-
S
-
1
1
-
- -
-
-
6
1
-
-
-
3
14
9
2
1
1
-
2
2
3
-
-
-
-
-
-
3
6
-
-
7
1
2
5
1
2
2
3
-
-
-
4
-
-
-
1
-
-
-
3
-
-
-
11
2
-
16
11
2
12
9
78
41
43
38
17
55
2
3
2
13
-
-
I
-
6
5
5
12
3
5
-
1
2
1
5
12
14
-
1
4
-
W
-
-
1
N
15
3
15
2
-
9
-
1
-
-
TN
-
F
4
23
-
0
5
2
-
D
-
-
1
B
-
3
-
1
1
1
1
2
1
I
-
5
10
1
1
1
30
7
1
4
23.1 Percent Correct out of
-
-
-
6
-
P
T
K
B
D
G
F
TH
S
SH
V
IN
Z
ZH
CH
J
N
I
R
W
L
y
H
WE
T
1
3
4
3
-
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
3
12
3
-
2
-
28
Three simulated normals
P
6
-
2
2
6
4
-
7
-
1
4
3
-
29
-
-
-
22
1
-
7
1
1
-
1
3
2
2
-
21
H W
1
-
-
-6
5
6
3
Y
-
2
9
-
9
-
-
-
2
3
4
-
I
3
3
6
L
1
S
1
5
1
-
9
5
6
U
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
-
6
54
54
54
4
47
-
2
10
8
54
54
54
54
-
-
10
54
66
47
1298
-
18
18
5
27
10
-
124 102
1
-
1
1
8
-
2
R
-
-
-
L
y
H
UN
2
17
I
1
-
1
2
1
-1
1
2
8
3
I
1
1
1
R
W
1
N
-
-1
-
I
J
2
-
1
ZN
CH
J
N
CH
-
z
2
2
2
ZN
-
-
Z
-
1
IN
-
I
-
V
-
-
SH
-
P
T
K
B
D
G
F
TH
S
SH
V
IH
S
-
TH
-
F
-
G
-
D
-
B
-
I
-
T
-
P
648 Presentations
21.8 Percent Correct out of
-
SIR
-
0 dB
Impairment PB, 98 dB, flat,
101
APPENDIX B. CONSONANT CONFUSION MATRICES
N
-
8
2
66
16
-
1
4
2
2
-
23
36
-
L
y
H
WE
-
-
-
-
5
-
-
-
-
-
3
-
-
-
-
-
-
4
-
-
2
3
42
16
44
22
27
50
Three simulated normals
P
P
T
I
B
D
G
F
TH
S
SH
V
II
Z
ZH
CH
J
m
N
R
W
L
y
H
WE
25
7
I
3
-
T
7
50
8
-
5
2
-
-
-
-
-
-
-
-
2
-
-
38
72
-
-
-
-
-
-
-
-
-
-
1
1
-
-
6-
--
--
--
--
--
--
-
2
4
-
-
-
3
-
-
4
-
-
-
2
3
-
-
-
-
-
-
-
-
1
-
-
7
-
-
5
-
2
1
-
2
25
20
-
2
3
4-
-
-
3
-
8
3
1
13
-
2
1
1
4
1
-
-
-
-
21
9
65
11
20
1
-
1
2
18
8
648
-
5
2
11
3
1
-
1
8
4
1
25
-
37
14
22
33
37
1
B
D
G
F
TH
S
SI
V
IN
Z
ZI
CH
J
N
I
R
V
L
Y
I
5
3
25
-
2
43
13
-
2
4
9
24
20
2
1
-
4
-
-
I
1
3
4
12
-
4
-
-
-
-
1
1
-
7
-
-
7
1
1
I
1
2
2
-
6
2
2
10
-
-
10
36
-
-
-
-
7
22
7
9
40
2
-
3
40
-
3
36
-
39
-
-
33
-
20
2
-
22
12
2
3
101
34
46
40
5
-
-
28
7
----- 1- -- 52
-1- --
-
-
-
8
6
14
15
9
46 116
14
-
1
7
24
-
2
3
-- -- - -4
-
----
-
77
1
-
38
-
5
69
-
--
12
37
-
2
-
50
-
11
6
I
6
-
2
-
I
-
-
5
1
9
4
17
-
-
3
-
-
1
4
-
43
-
1
---
--
-
-
--
54
39
14
78
54
52
1
-
-
1
-
1
5
-
3
I
1296 Presentations
I
27
9
- -
-
-
5
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
-1
4
1
3
4
2
115
I
-
65.9 Percent Correct out of
-I
-
1
2
1
6
-10
1 1 1
5 3
1
-
-
-
3
-
2
2
1
1
-
-2
2
- -
-
2
-
-
-
2
I
-
R
W
2
-
-
3
-
1
-
-
1
-
-
-
-
- -
-
2
42-
WE
-
22
H
- -
10
-
-
T
- -
-
3
2
-
L
-
-
17
1
6
V
-
-
-
R
-
23
I
1
-
1
17
7
-
6
-
6
31
2
-
22
-
57
83
56
47
63
-
7
-
1
W
- - -
1
-
2
-
N
54
- -
2
-
3
1
-
3
4
3
-
14
-
2
1
-
2
3
2
-
-
-
1
1
J
54
54
54
54
-
12
11
17
-
CH
54
1
- - -
1
-
ZE
- -
-
-
Z
54
54
54
54
54
54
54
- - - -
-
II
-
1
V
-
SH
648 Presentations
of
12
-
S
-
TH
-
F
-
1
2
5
13
3
G
-
3
6
1
-
D
- -
13
22
22
B
-
3
-
I
-
T
-
P
T
K
B
D
G
F
TH
S
SH
v
II
Z
ZI
CH
P
35.3 Percent Correct out
-
Impairment PB, 98 dB, EFE, Quiet
102
1
54
54
54
54
54
54
54
54
54
54
11
54
25
1296
APPENDIX B. CONSONANT CONFUSION MATRICES
-
1
71
35
1
10
-
-18
-
2
4
1
1--
-
1
1
-
---
-
-
6
-
-
1
-
40
16
2
1 17
9
31
-
35
22
-
-
17
1
-
-
-
5
6
1
-
2
y
N
WE
-
14
-
-
-
-
13
-
7
4
-
-
-
5
-
-
-
2
-
-
-
3
-
-
-
-
4
-
42
68
40
85
75
2
-
S -
-
1-
-
-
-
-
-
-
-
- 47
-
5
2
-
-2
-
-
-
-
-2
-
-
2
1
49
90
7
39
2
6
-
-
1-
-
2
- -
2
-
SN
V
-
2-
17
IN
18
26
1
-
23
--
N
R
V
L
Y
H
3
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
2
11
-
-
3
-
4
-
-
3
3
2
-
11
1
-
8
-
9
2
36
5
35
9
28
-
82
S
-
I
6
-
-
-
-
-
-
-
-
-
-
1
-
2
-
-
-
1
-
4
13
-
3
10
-
-
49
850
4
-
7
-
-
-
-
1
3
17
2
5
-
52
-
3
-
-
-38
-15
-53
1 34 17
- -
-
-
----
-
---
-
-
11
75
-
-
--
-
-
---
-
-
-
--
-
45 102
42
7
11
3
-
648
1296 Presentations
N
2
11
-
9
J
-
-
40
CH
-
-
50
ZE
-
-
26
Z
-
-
L
S
-
-
ZH
CN
M
N
R
v
-
-37
-
J
59
14
-
-
6
-
26
-
9
27
-
9
10
4
- -
z
4
3
I
-
-
1
-
11
29
-
1
1
8
-
-
10
38
46
69
4
-1
-
-
-
1
8
5
-
-
6
1
3
5
9
1
34
-
47
4
4
-
22
-
65
55
56
WE
- -
34
3
-23-
1
-
4
4
-
-
4
-
3
1
42
-
4
4
1
-
-
3
2
-
-
2
16
--
1
24
-
I
2
1
3
-
2
-
I
1
-
S
-
1
-
14
1
TH
-
1
-
I
4
4
F
-
5
-
-
1
-
G
6
-
F
TH
S
SH
V
IN
52
D
-
-
1
2
3
-
-
B
D
G
27
-
B
-7
54.7 Percent Correct out of
-
K
I
3
-
-
-
P
T
T
1
-
-
1
Three simulated normals
P
-1
- -
29
-
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
27
-
28
1
1
H WH
Y
1
-
25
L
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
5
64
64
64
64
S4
64
16
1296
-
11
V
8
- -
23
R
-
WE
1
I
-
-
-
1
1
I
43
3
10
-
-
1
-
4
-
2
7
16
-
2
2
1
4
-
1
1
2
K
-
-
7
17
2
6
CH
- -
4
-
8
ZH
-
-
Z
-
2
IN
-
-
1
V
-
5
3
3
11
3
1
2
9
12
-
R
V
L
y
H
1
-
-
1
2
1
-
1
2
SH
-
1
S
-
-
2
7
4
1
1
TH
-
16
3
F
-
15
-
z
ZN
CH
J
G
4
-
-
D
-
3
B
-
P
T
K
B
D
G
F
TH
S
SH
V
IN
I
T
648 Presentations
29.6 Percent Correct out of
-
P
0 dB SIR
-
HFE,
Impairment PB, 98 dB,
103
APPENDIX B. CONSONANT CONFUSION MATRICES
-
-
-
-
-
-
-
-
-
-
-
6
1
3
3
-
-
1
-
2
-
-
-
-
-
2
-
1
5
1
2
2
-
-
2
9
2
-
3
-
1
1
-
1
-
-
18
12
-9
-
-
-
-
--
-
1
-
13-1
-
2
8
2
-
29
0
33
26
Two simulated normals
N
N
R
V
11
-
-
5
2
-
-
-
23
7
-
-
1
13
-
-
1
1
-
13
1
6
-
-
-
-
-
-
-
2
2
-
5
-
2
-
5
1
1
1
-
ZN
CH
J
K
1
8
I
-
-
7
-
-
-
2
2
-
-
-
-
-
-2
1
-
-
-
2-
-
-
-
-
-2
-
-
-
-
-2
-
-
-
1
27
-
1
-1
-
33
8
1
3
-
-
-
2
1
11-
23
10
11
-
-
27
43
17
39
-
11
8
6
-
-
-
3
2
1
13
36
47
17
3
-
-
-
-
-
-
-
2-
-
24
10
2
23
-
4
1
27
17
10
34
43
3
9
30
-
-
-
-
53
35
1
3
46
432
864 Presentations
-
Z
34
2
-
21 Ill
22
-
IN
-
1
1
2
V
L
y
N
WV
11
-
-
7
-
-
SH
1
-
-
-
-
2
-
11
2
-
1
I
4
2
-
-
2
1
6 18
-
-
15
-
-
-
-
6
28
18
R
V
L
Y
N
2
5
1
-
5
1
2
-
2
2
-
2
2
3
7
-
1
5
-
-
5
2
-
11
1
1
-
-
-
-
2
-
-
7
4
1
8
1
1
1
-
1
-
V
1
3
2
-
-
10
-
-
53
39
36
36
36
36
36
36
36
36
36
36
36
36
36
1
36
36
36
36
36
36
36
-
-
4
713
30
78
-
2
2
70
-
-
J
1
22
-
-
-
F
TH
S
SH
V
IN
z
ZH
CH
-
3
6
S
-
-
1
- -
-
6
-
TH
-
B
D
G
-
-
F
-
1
4
16
-
- -
-
-
G
-
I
D
-
2
29
B
-
10
-
I
-
P
T
4
46.6 Percent Correct out of
-
T
5
1
-
-
P
1
-
-
5
15
2
-
15
-
4
-
10
-
20
-
-
- -
31
1
-
15
1
-
16
-
2
-
-
1
--
2
1
-
-
L
y
H
WE
-
-
1
-
-
3
13
-
2
1
-
W
6
3
-
R
-
-1
1
N
12
3
-
3
1
-
-
-
-
2
4
-
-
1
-
-
-
I
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
-
-
1
5
1
WE
-
2
-
N
-
1
Y
-
6
4
-I
-1
L
-
1
2
-
R
-
4
7
-
I
-
3
N
V
J
-
-
-
2
CH
-
7
ZN
-
-
-
Z
-
-
IN
-
3
-
2
-
2
-
V
432 Presentations
-
-
-
5
2
11
-
-
2
12
1
-
5
1
2
-
5
4
SH
-
1
S
-
-
TH
-
-
F
-
G
-
z
ZN
CH
D
-
5
10
2
B
39.4 Percent Correct out of
-
2
1
I
-
P
T
K
B
D
G
F
TH
S
SH
V
IN
T
Quiet
-
P
PB, 104 dB, flat,
-
Impairment
104
-
-
2
36
36
36
36
48
34
14
864
3
APPENDIX B. CONSONANT CONFUSION MATRICES
4
4
1
-
1
3
1
N
WE
-
-1
-1
1
1
2
2
11
4
1
-
-
5 15
39
5
25
-
18
1
3
2
-
-
1
-
-
-
-
-
1
-
-
-
1
-
17
4
--
--
-
4
-
1
3
-
1
1
-
6
31
27
73
TH
S
SE
2
6
6
-
-
-
24
CH
-
-
10
1
-
2
-
25
15
13
1
3
-
3
2
2
1
-
4
-
-
6
11
9
-
-
2
-
9
5
-4
3
-
-
-
-
-
-
- -
1
1
-
-
1
1
1
-
-
-
-
7
-
-
-
1
-
-
-
1
-
-
2
4
-
-
58
13
70
It
21
32
64
14
N
N
R
V
L
Y
N
-
-
-
2
1
-
-
4
2
WE
-
-
-
-
1
19
7
2
-
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
1
1
19
-
1
36
22
-
-
-
-
1
-
-
1
6
-
3
3
2
9
2
-
-
-
1
-
2
--
4
-
1
30
1
2
-
-
5
-
3
10
-
-
2
2
-
8
7
-
--
5
-
-
-
36
-
-
18
-
-
1
-
--
35
-
22
-
-
9
22
-
28
0
9
30
66
1-2
2
11
5
-
-
-
I
4
2
-
66
45
-
5
18
432
864 Presentations
J
-
8
28
1
4
8
3
35
-
4
3
IN
6
-
6
- - -
10
-
-
ZN
-
-
Z
6 2
9
1
-
V
-
2
F
-
-
1
G
2
-
17
6
-
--
-
17
-
-
4
13
2
-
20
17
2
2
--
4
-
-
3
3
10
-
-
-
-
-
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
-
-
1
1
3
13
1
-
18
18
-
3
-
6
-
6
-
D
4
3
-
-
-
14
-
B
3
18
18
18
18
18
18
1
- -
12
2
-
2
K
-
T
1
-
WE
-
L
y
-
-
N
I
R
W
1
-
J
1
42.9 Percent Correct out of
P
-
R
-
16
- -
ZH
CH
2
- -
17
-
z
2
-
11
-
F
TH
S
SN
V
IN
-
-
B
D
G
-
3
1
-
-1
Two simulated normals
I
-
12
Y
1
1
3
2
L
-
1
-
6
-
6
17
1
35
2
-
33
72
45
46
-
1
-
-
3
V
1
36
36
36
36
-
W
L
Y
N
WE
R
3
R
P
T
-1
-
-3-
1
10
-
-
N
4
5
1
-
7
-12
1
1
-
I
12
-
-
-
N
-
1
2
-
1
J
-
3
1
2
CH
-
2
-13
IN
ZE
ZN
CH
ZH
-
-
Z
-
1
IN
-
2
2
V
-
1
-
S SN
-
-
p
T
K
B
D
G
F
TH
S
SN
TH
-
F
-
G
432 Presentations
-
D
-
B
-
I
29.6 Percent Correct out of
-
T
SdB
-
P
3
-
Impairment PB, 104 dB, flat, 0
105
45
18
864
APPENDIX B. CONSONANT CONFUSION MATRICES
2
--
-
-
-
-21-
1
-
3
1
-
-
-
-
-
-
2
3
2
-
1
--
--
-
2
-
17
-
2
-
1
-6
1
-
1
16
1
3
22
53
36
13
6
24
-
-
-
-
3
4
26
20
-
-
-
-
-
5
36
-
-
-
3
-
9
-
-
-
-
22
-
--
-
12
-
--
14
1
-
-
5
--
--
-1
IN
Z
-
-
-
1
-
-
-
-
ZE
-
44
26
33
56
24
66
-
19
36
17
14
17
18
18
18
22
8
11
N
WE
R
V
L
Y
-2
-
-
-
-
-
-
-
9-
6
6
-
-
-
-
-
1-
-
-
-
-
36
36
36
36
36
36
36
36
36
36
36
-1
--
13-4
2
3
36
-9
-
-2
-
-7
1
--
-
-
--
-
-
-
--
1
2-
-
-1
351
-
1
29-
-
-1
36
-
2
-
1-
-
-4
1
-
-1
-
530
---
3-
2
37
17
2
37
30
49
432
864 Presentations
-
29-
-
-
6
-
5
-32
-27
-
6
5
-
1
4
-
2
4
25
2
-
-
-
36
-
1
N
--30-
-5
4
N
36
-
--
2
18
12
J
-
--
-
CH
24
3
3
4
4
-
-
-
15
4
-
17
-
- -
3
21
-
V
- - -
36
4
3
SH
-
23
-
-
3
4
18
18
-
S
-
41
2
18
2
1
9
- -
TH
-35
-
1
2
-
-
F
2
-
20
-
-
G
-
D
-
3
-
B
1
-
3
2
-
I
-
-
-
2
8
2
-
36
-
30
--
T
1-
2
10
70.6 Percent Correct out of
P
2
1
-
Two simulated normals
4
-
- - -
-
2
-
-
-
1
1
18
18
-
24
-
4
-
12
2
1
18
18
18
18
36
36
36
36
36
36
36
36
36
36
36
36
-
16
-
31
-
-
18
-
3
18
- - - 16
- -
34
-
-
2
18
18
- - -
1
-
7
-
1
-
-
L
y
H
WE
-
18
18
18
1
2
21
7
3
-
4
--
-I
R
-
-2
N
I
P
T
I
B
D
G
F
TH
S
SH
V
IN
Z
ZN
CH
J
N
N1
R
W
L
y
H
WE
-1
18
18
18
18
18
-
16
-
-
-I
-
-
1
-
H WE
-
-
-
Y
-
-
2
2
L
-
1-
9
-
V
-
-
-
I
-
-
R
-
-
-
1
8-
-
-
-
I
-
-
-
-
-
-
-
N
-
-0
1
-
-
I
1
J
-
-
CH
-
1
-
-
-- - - -
-
ZN
-
IN
6
Z
-
I
-
SH
432 Presentations
-
S
-
TH
16
11
-
F
-
1
G
-
1
D
41.4 Percent Correct out of
-
1
B
-
4
-
K
-
P
T
K
B
D
G
F
TH
S
SH
V
IN
z
ZH
CH
T
Quiet
-
P
HFE,
-
Impairment PB, 104 dB,
106
42
41
44
32
62
47
39
40
19
9
864
APPENDIX B. CONSONANT CONFUSION MATRICES
SH
-
-
1
-
-
-
-
-
-
-
4
-
-
-
CH
-
7
2
-
-
2
1
-
1
2
-
2
-
2
-
-
1
-
-
-
-
1
1
-
-
13
-
-
1
7
-
-
-
1
-
-
I
-
-
1
2
- -
1
-
14
-16
1
7
1
1
7
-
1
-
2
-
1
-
-
-
1
--
-
4
--
3-4
-
4
-3
2
2-
-
3
1
1
1
-
H
Y
6
W
-I
18
1
3
2
18
18
18
4
1
18
3
1
1
1
-
1
-
-
-
2
-
1
3
-
1
-
2
-2
82
1
2
1
6
2
1
-
-
3-
3
-
15
2
-
--
-
1
-
4
-
-
-
1-1
-
4
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
18
14
432
-
1
-
1
18
26
28
31
16
1
3
39
D
G
F
TH
S
-
3
-
-
-
9
-
-
-
36
1
20
-
8
-
-
-
1
--
34-
-
13--
--
--
- -- - -
-
-
-
J
-
8
-
11
-
7
-
-
-
-
---
- -36
9
-
2
-
4
N
I
R
W
4
1
-
-2
-
1
--
1
-
1
-36
9
--
-
-
--
32
49
24
3
-
-
-
1
-
-
39
26
31
73
20
37
-
-
-
-
-
27
864 Presentations
J
N
I
R
V
L
Y
N
-
-
-
-
-
-
-
-
-
-
-
3
-
-
2
-
2
-
-
1
-
-
-
6
2
-
-
-
-
-
3
1
3
-
2
2
1
1
321 -- -- -- - - -1- 29- 361
-
6-
- - - - -
-
-
-
1
-
30
CH
-
-
27
ZN
1
36
Z
2
7
IN
1
L
y
N
WE
19
-
-
10
2
I
-
12
-
-
WN
-
-
5
220---- - - 26
I
8 23- - - - 4
1 21
2
2
6
2
4
3
-
4
-
-
30
4
23
2
24
-
1
36
-
22
-
62
57
38
38
30
1
36
25
4
40
30
58
33
47
27
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
36
3
36
36
8
864
-
1
5
8
14
-
-
4
30
-
-
-
-
1 231 -
-
1
-
7
-
-
4
-
4
- -
B
D
G
V
-
3
-
I
-
SH
1
- - -
B
8
- - -
I
21
10
66.1 Percent Correct out of
T
P
T
20
4
-
-
-
-
-
- -
1
-
-
-
1
10
- -
2
-
-
-
-
-
-
3
13
-
2
-
-
1
-
Two simulated normals
P
I
3
-5
-
4
-
-
17
2
-
14
-
-
12
2
1
-
13
-
1
-
28
1
-
-
2
4
-
2
2
-
2
-
-
1
18
18
-
L
-
1
V
-
-
-
3
-
-
-I
-
-
-
1
1
R
-
8
6
2
I
-
6
1
2
-
10
N
- -
16
-
J
-
ZE
-
Z
2
-
5
F
TH
S
SH
V
IN
Z
ZN
CH
IN
-
V
-
S
-
TH
-
F
G
-
D
432 Presentations
-
B
38.7 Percent Correct out of
-
3
I
SIR
-
P
T
I
B
D
G
F
TH
S
SH
v
IN
Z
ZN
CH
J
N
I
R
W
L
y
H
WE
T
0 dB
-
P
HFE,
-
Impairment PB, 104 dB,
107
4