Phonetic features in ASR
Intensive course Dipartimento di Elettrotecnica ed Elettronica
Politecnica di Bari
22 – 26 March 1999
Jacques Koreman
Institute of Phonetics
University of the Saarland
P.O. Box 15 11 50
D - 66041 Saarbrücken
Germany
E-mail:
jkoreman@coli.uni-sb.de
Organisation of the course
• Tuesday – Friday:
- First half of each session:
theory
- Second half of each session: practice
• Interruptions invited!!!
Overview of the course
1. Variability in the signal
2. Phonetic features in ASR
3. Deriving phonetic features from the
acoustic signal by a Kohonen network
4. ICSLP’98: “Exploiting transitions and
focussing on linguistic properties for ASR”
5. ICSLP’98: “Do phonetic features help to
improve consonant identification in ASR?”
The goal of ASR systems
• Input: spectral description of microphone
signal, typically
- energy in band-pass filters
- LPC coefficients
- cepstral coefficients
• Output: linguistic units, usually phones or
phonemes (on the basis of which words can
be recognised)
Variability in the signal (1)
Main problem in ASR: variability in the input
signal
Example: /k/ has very different realisations
in different contexts. Its place of
articulation varies from velar before
back vowels to pre-velar before front
vowels
(own articulation of “keep”,“cool”)
Variability in the signal (2)
Main problem in ASR: variability in the input
signal
Example: /g/ in canonical form is sometimes
realised as a fricative or approximant ,
e.g. intervocalically (OE. regen > E.
rain). In Danish, this happens to all
intervocalic voiced plosives; also,
voiceless plosives become voiced.
Variability in the signal (3)
Main problem in ASR: variability in the input
signal
Example: /h/ has very different realisations
in different contexts. It can be
considered as a voiceless realisation
of the surrounding vowels.
(spectrograms “ihi”, “aha”, “uhu”)
Variability in the signal (3a)
[ i: h
i: ]
[ a: h a:
]
[ u:
h u:
]
Variability in the signal (4)
Main problem in ASR: variability in the input
signal
Example: deletion of segments due to articulatory overlap. Friction is superimposed
on the vowel signal.
(spectrogram G.“System”)
Variability in the signal (4a)
p0
s
i m
a
l
z Y s p0 t
(
[ b0 d e
e
m]
Variability in the signal (5)
Main problem in ASR: variability in the input
signal
Example: the same vowel /a:/ is realised differently dependent on its context.
(spectrogram “aba”, “ada”, “aga”)
Variability in the signal (5a)
[ a: b0b a: ]
[ a: b0d
a: ]
[ a: b0g a: ]
Modelling variability
• Hidden Markov models can represent the
variable signal characteristics of phones
1-p1
S
1
1-p2
p1
1-p3
p2
p3
E
Lexicon and language model (1)
• Linguistic knowledge about phone
sequences (lexicon, language model)
improves word recognition
• Without linguistic knowledge, low phone
accuracy
Lexicon and language model (2)
Using a lexicon and/or language model is not a
top-down solution to all problems: sometimes
pragmatic knowledge needed.
Recognise speech
Example: [rsp]
Wreck a nice beach
Lexicon and language model (3)
Using a lexicon and/or language model is not a
top-down solution to all problems: sometimes
pragmatic knowledge needed.
Get up at eight o’clock
Example: [p]
Get a potato clock
CONCLUSIONS
• The acoustic parameters (e.g. MFCC) are
very variable.
• We must try to improve phone accuracy by
extracting linguistic information.
• Rationale: word recognition rates will
increase if phone accuracy improves
• BUT: not all our problems can be solved
Practical:
Phonetic features in ASR
• Assumption: phone accuracy can be
improved by deriving phonetic features from
the spectral representation of the speech
signal
• What are phonetic features?
A phonetic description of sounds
• The articulatory organs
A phonetic description of sounds
• The articulation of consonants
velum (= soft palate)
tongue
A phonetic description of sounds
• The articulation of vowels
Phonetic features: IPA
• IPA (International Phonetic Alphabet) chart
- consonants and vowels
- only phonemic distinctions
(http://www.arts.gla.ac.uk/IPA/ipa.html)
The IPA chart (consonants)
The IPA chart (other consonants)
The IPA chart (non-pulm. cons.)
The IPA chart (vowels)
The IPA chart (diacritics)
IPA features (obstruents)
p0
b0
p
t
k
b
d
g
f
T
s
S
C
x
vfri
vapr
Dfri
z
Z
l
a
b
0
0
1
-1
-1
1
-1
-1
1
-1
-1
-1
-1
-1
1
1
-1
-1
-1
d
e
n
0
-1
-1
-1
-1
-1
-1
-1
1
1
-1
-1
-1
-1
1
1
1
-1
-1
a
l
v
0
0
-1
1
-1
-1
1
-1
-1
-1
1
1
-1
-1
-1
-1
-1
1
1
p
a
l
0
0
-1
-1
-1
-1
-1
-1
-1
-1
-1
1
1
-1
-1
-1
-1
-1
1
v
e
l
0
0
-1
-1
1
-1
-1
1
-1
-1
-1
-1
-1
1
-1
-1
-1
-1
-1
u
v
u
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
g
l
o
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
p
l
o
1
1
1
1
1
1
1
1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
f
r
i
0
0
-1
-1
-1
-1
-1
-1
1
1
1
1
1
1
1
-1
1
1
1
n
a
s
0
0
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
l
a
t
0
0
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
a
p
r
0
0
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
1
-1
-1
-1
t
r
i
0
0
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
v
o
i
-1
1
-1
-1
-1
1
1
1
-1
-1
-1
-1
-1
-1
1
1
1
1
1
IPA features (sonorants)
m
n
J
N
l
L
rret
ralv
Ruvu
j
w
h
...
l
a
b
1
-1
-1
-1
-1
-1
-1
-1
-1
-1
1
-1
0
d
e
n
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
0
a
l
v
-1
1
-1
-1
1
-1
1
1
-1
-1
-1
-1
0
p
a
l
-1
-1
1
-1
-1
1
-1
-1
-1
1
-1
-1
0
v
e
l
-1
-1
-1
1
-1
-1
-1
-1
-1
-1
1
-1
0
u
v
u
-1
-1
-1
-1
-1
-1
-1
-1
1
-1
-1
-1
0
g
l
o
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
1
0
p
l
o
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
0
f
r
i
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
1
0
n
a
s
1
1
1
1
-1
-1
-1
-1
-1
-1
-1
-1
0
l
a
t
-1
-1
-1
-1
1
1
-1
-1
-1
-1
-1
-1
0
a
p
r
-1
-1
-1
-1
1
1
1
-1
-1
1
1
-1
0
t
r
i
-1
-1
-1
-1
-1
-1
-1
1
1
-1
-1
-1
0
v
o
i
1
1
1
1
1
1
1
1
1
1
1
-1
0
A zero value is assigned to all vowel features (not listed here)
IPA features (vowels)
m
i
d
i
-1
y
-1
u
-1
e
1
o
1
V
1
Uschwa 1
a
-1
E
1
3
1
@
1
o
p
e
-1
-1
-1
-1
-1
1
-1
1
1
1
1
f
r
o
1
1
-1
1
-1
-1
-1
1
1
1
-1
c
e
n
-1
-1
-1
-1
-1
-1
1
1
-1
1
1
r
o
u
-1
1
1
-1
1
-1
1
-1
-1
-1
-1
I
Y
U
2
O
Q
{
A
9
m
i
d
-1
-1
-1
1
1
-1
-1
-1
1
6
-1
o
p
e
-1
-1
-1
-1
1
1
1
1
1
f
r
o
1
1
-1
1
-1
-1
1
-1
1
c r
e o
n u
1 -1
1 1
1 1
-1 1
-1 1
-1 1
-1 -1
-1 -1
-1 1
1 -1
1 -1
A zero value is assigned to all consonant features (not listed
here)
Phonetic features
• Phonetic features
- different systems (JFH, SPE, art. feat.)
- distinction between “natural classes”
which undergo the same phonological
processes
SPE features (obstruents)
p0
b0
p
b
tden
t
d
k
g
f
vfri
T
Dfri
s
z
S
Z
C
x
c
n
s
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
s
y
l
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
n
a
s
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
s
o
n
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
l
o
w
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
h
i
g
0
0
-1
-1
-1
-1
-1
1
1
-1
-1
-1
-1
-1
-1
1
1
1
1
c
e
n
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
b
a
c
0
0
-1
-1
-1
-1
-1
1
1
-1
-1
-1
-1
-1
-1
-1
-1
-1
1
r
o
u
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
a
n
t
0
0
1
1
1
1
1
-1
-1
1
1
1
1
1
1
-1
-1
-1
-1
c
o
r
0
0
-1
-1
1
1
1
-1
-1
-1
-1
1
1
1
1
1
1
-1
-1
c
n
t
-1
-1
-1
-1
-1
-1
-1
-1
-1
1
1
1
1
1
1
1
1
1
1
v
o
i
-1
1
-1
1
-1
-1
1
-1
1
-1
1
-1
1
-1
1
-1
1
-1
-1
l
a
t
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
s
t
r
-1
-1
-1
-1
-1
-1
-1
-1
-1
1
1
-1
-1
1
1
1
1
1
1
t
e
n
1
-1
1
-1
1
1
-1
1
-1
1
-1
1
-1
1
-1
1
-1
1
1
SPE features (sonorants)
m
n
J
N
l
L
ralv
Ruvu
rret
j
vapr
w
h
XXX
c
n
s
1
1
1
1
1
1
1
1
1
-1
-1
-1
-1
0
s
y
l
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
0
n
a
s
1
1
1
1
-1
-1
-1
-1
-1
-1
-1
-1
-1
0
s
o
n
1
1
1
1
1
1
1
1
1
1
1
1
1
0
l
o
w
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
1
0
h
i
g
-1
-1
1
1
-1
1
-1
-1
-1
1
-1
1
-1
0
c
e
n
0
0
0
0
0
0
0
0
0
0
0
0
0
0
b
a
c
-1
-1
-1
1
-1
-1
-1
1
-1
-1
-1
1
-1
0
r
o
u
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
1
-1
0
a
n
t
1
1
-1
-1
1
-1
1
-1
-1
-1
1
1
-1
0
c
o
r
-1
1
-1
-1
1
-1
1
-1
1
-1
-1
-1
-1
0
c
n
t
-1
-1
-1
-1
1
1
1
1
1
1
1
1
1
0
v
o
i
1
1
1
1
1
1
1
1
1
1
1
1
-1
0
l
a
t
-1
-1
-1
-1
1
1
-1
-1
-1
-1
-1
-1
-1
0
s
t
r
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
0
t
e
n
0
0
0
0
0
0
0
0
0
0
0
0
0
0
SPE features (vowels)
c
n
s
i
-1
I
-1
e
-1
E
-1
{
-1
a
-1
y
-1
Y
-1
2
-1
9
-1
A
-1
Q
-1
V
-1
O
-1
o
-1
U
-1
u
-1
Uschwa -1
3
-1
@
-1
6
-1
s
y
l
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
n
a
s
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
s
o
n
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
l
o
w
-1
-1
-1
-1
1
1
-1
-1
-1
-1
1
1
-1
-1
-1
-1
-1
-1
-1
-1
1
h
i
g
1
1
-1
-1
-1
-1
1
1
-1
-1
-1
-1
-1
-1
-1
1
1
-1
-1
-1
-1
c
e
n
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
1
1
1
1
b
a
c
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
1
1
1
1
1
1
1
-1
-1
-1
-1
r
o
u
-1
-1
-1
-1
-1
-1
1
1
1
1
-1
1
-1
1
1
1
1
1
-1
-1
-1
a
n
t
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
c
o
r
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
c
n
t
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
v
o
i
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
l
a
t
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
s
t
r
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
t
e
n
1
-1
1
-1
-1
1
1
-1
1
-1
-1
-1
-1
-1
1
-1
1
-1
1
-1
-1
CONCLUSION
• Different feature matrices have different
implications for relations between phones
Practical:
Kohonen networks
• Kohonen networks are unsupervised neural
networks
• Our Kohonen networks take vectors of
acoustic parameters (MFCC_E_D) as input
and output phonetic feature vectors
• Network size: 50 x 50 neurons
Training the Kohonen network
1. Self-organisation results in a phonotopic
map
2. Phone calibration attaches array of phones
to each winning neuron
3. Feature calibration replaces array of
phones by array of phonetic feature vectors
4. Averaging of phonetic feature vectors for
each neuron
Mapping with the Kohonen network
• Acoustic parameter vector belonging to
one frame activates neuron
• Weighted average of phonetic feature
vector attached to winning neuron and
K-nearest neurons is output
Advantages of Kohonen networks
• Reduction of features dimensions possible
• Mapping onto linguistically meaningful
dimensions (phonetically less severe
confusions)
• Many-to-one mapping allows mapping of
different allophones (acoustic variability)
onto the same phonetic feature values
• automatic and fast mapping
Disadvantages of Kohonen networks
• They need to be trained on manually
segmented and labelled material
• BUT: cross-language training has been
shown to be succesful
Hybrid ASR system
phone
lexicon
language model
hidden Markov
modelling
phonetic features
Kohonen network
Kohonen
Kohonennetwork
network
MFCC’s + energy
delta parameters
phone
CONCLUSION
• Acoustic-phonetic mapping extracts
linguistically relevant information from the
variable input signal.
Practical:
ICSLP’98
Exploiting transitions and focussing on
linguistic properties for ASR
Jacques Koreman
William J. Barry
Bistra Andreeva
Institute of Phonetics, University of the Saarland
Saarbrücken, Germany
INTRODUCTION
Variation in the speech signal caused by coarticulation between sounds is one of the main challenges in
ASR.
• Exploit variation if you cannot reduce it
Coarticulatory variation causes vowel transitions
to be acoustically less homogeneous, but at the
same time provides information about neighbouring sounds whichcan be exploited (experiment 1).
• Reduce variation if you cannot exploit it
Some of the variation is not relevant for the phonemic identity of the sounds. Mapping of acoustic
parameters onto IPA-based phonetic features like
[± plosive] and [± alveolar] extracts only linguistically relevant properties before hidden Markov
modelling is applied (experiment 2).
INTRODUCTION
No lexicon or language model
The controlled experiments presented here reflect our general
aim of using phonetic knowledge to improve the ASR system
architecture. In order to evaluate the effect of the changes in
bottom-up processing, no lexicon or language model is used.
Both improve phone identification in a top-down manner by
preventing the identification of inadmissible words (lexical
gaps or phonotactic restrictions) or word sequences.
DATA
Texts
English, German, Italian and Dutch texts from the EUROM0
database, read by 2 male + 2 female speakers per language
Hamming window: 15 ms
step size: 5 ms
pre-emphasis: 0.97
DATA
Signals
• 12 mel-frequency cepstral coefficients (MFCC’s)
• energy
• corresponding delta parameters
Hamming window: 15 ms
step size: 5 ms
pre-emphasis: 0.97
16 kHz microphone signals
DATA
Labels
• Intervocalic consonants labelled with SAMPA symbols,
except plosives and affricates, which are divided into
closure and frication subphone units
• 35-ms vowel transitions labelled as
i_lab, alv_O (experiment 1)
V_lab, alv_V (experiment 2)
where lab, alv = cons. generalized across place
V
= generalized vowel
Hamming window: 15 ms
step size: 5 ms
pre-emphasis: 0.97
EXPERIMENT 1: SYSTEM
consonant
lexicon
hidden Markov
modelling
language model
Hamming window: 15 ms
step size: 5 ms
pre-emphasis: 0.97
MFCC’s + energy +
delta parameters
MFCC’s + energy +
delta parameters
C
Voffset - C - Vonset
EXPERIMENT 1: RESULTS
consonant
41,97
46,79
26,57
15,83
44,78
no V transitions
V transitions
13,17
100
90
80
70
60
50
40
30
20
10
0
place
manner
EXPERIMENT 1: CONCLUSIONS
When vowel transitions are used:
• consonant identification rate improves
• place better identified
• manner identified worse, because hidden Markov
models for vowel transitions generalize across
all consonants sharing the same place of articulation (solution: do not pool consonants sharing
the same place of articulation)
• vowel transitions can be exploited for identification of the
consonant, particularly its place of articulation
EXPERIMENT 2: SYSTEM
consonant
lexicon
hidden Markov
modelling
language model
phonetic features
Kohonen
network
Kohonen
Kohonen
network
network
MFCC’s + energy
delta parameters
C
EXPERIMENT 2: RESULTS
consonant
26,57
46,79
52,00
66,12
77,70
no mapping
mapping
13,17
100
90
80
70
60
50
40
30
20
10
0
place
manner
EXPERIMENT 2: CONCLUSIONS
When acoustic-phonetic mapping is applied:
• consonant identification rate improves strongly
• place better identified
• manner better identified
• phonetic features better address linguistically relevant
information than acoustic parameters
EXPERIMENT 3: SYSTEM
consonant
lexicon
hidden Markov
modelling
language model
phonetic features
Kohonen
network
MFCC’s + energy
C
Kohonen
Kohonen
network
network
delta parameters
Voffset - C - Vonset
consonant
place
76,67
67,71
66,12
52,23
mapping, no V transitions
mapping; V transitions
52,00
100
90
80
70
60
50
40
30
20
10
0
77,70
EXPERIMENT 3: RESULTS
manner
EXPERIMENT 3: CONCLUSIONS
When transitions are used for acoustic-phonetic
mapping:
• consonant identification rate does not improve
• place identification improves slightly
• manner identification rate decreases slightly
vowel transitions do not increase identification rate:
• because baseline identification rate is already high
• vowel transitions are undertrained in the Kohonen networks
INTERPRETATION (1)
• The greatest improvement in consonant identification is
achieved in experiment 2. By mapping acoustically different
realisations of consonants onto more similar phonetic
features, the input to hidden Markov modelling becomes more
homogeneous, leading to a higher consonant identification
rate.
• Using vowel transitions also leads to a higher consonant
identification rate in experiment 1. It was shown that
particularly the consonants’ place is identified better. Findings
confirm the importance of transitions as known from
perceptual experiments.
INTERPRETATION (2)
• The additional use of vowel transitions when acoustic-phonetic
mapping is applied does not improve the identification results.
Two possible explanations for this have been suggested:

the identification rates are high anyway when mapping is
applied, so that it is less likely that large improvements are
found
 the generalized vowel transitions are undertrained in the
Kohonen networks, because the intrinsically variable frames
are spread over a larger area in the phonotopic map.
The latter interpretation is currently being verified by Sibylle
Kötzer by applying the methodology to a larger database
(TIMIT).
REFERENCES (1)
Bitar, N. & Espy-Wilson, C. (1995a). Speech parameterization
based on phonetic features: application to speech recognition.
Proc. 4th Eurospeech, 1411-1414.
Cassidy, S & Harrington, J. (1995). The place of articulation
distinction in voiced oral stops: evidence from burst spectra
and formant transitions. Phonetica 52, 263-284.
Delattre, P., Liberman, A. & Cooper, F. (1955). Acoustic loci
and transitional cues for consonants. JASA 27(4), 769-773.
Furui, S. (1986). On the role of spectral transitions for speech
preception. JASA 80(4), 1016-1025.
Koreman, J., Andreeva, B. & Barry, W.J. (1998). Do phonetic
features help to improve consonant identification in ASR?
Proc. ICSLP.
REFERENCES (2)
Koreman, J., Barry, W.J. & Andreeva, B. (1997). Relational
phonetic features for consonant identification in a hybrid ASR
system. PHONUS 3, 83-109. Saarbrücken (Germany): Institute
of Phonetics, University of the Saarland.
Koreman, J., Erriquez, A. & W.J. Barry (to appear
). On
the selective use of acoustic parameters for consonant
identification. PHONUS 4. Saarbrücken (Germany): Institute
of Phonetics, University of the Saarland.
Stevens, K. & Blumstein, S. (1978). Invariant cues for place of
articulation in stop consonants.
JASA 64(5), 1358-1368.
SUMMARY
• Acoustic-phonetic mapping by a Kohonen
network improves consonant identification
rates.
Practical:
ICSLP’98
Do phonetic features help to improve
consonant identification in ASR?
Jacques Koreman
Bistra Andreeva
William J. Barry
Institute of Phonetics, University of the Saarland
Saarbrücken, Germany
INTRODUCTION
Variation in the acoustic signal is not a problem for human
perception, but causes inhomogeneity in the phone models for
ASR, leading to poor consonant identification. We should
“directly target the linguistic information in the signal
and ... minimize other extra-linguistic information
that may yield large speech variability”
(Bitar & Espy-Wilson 1995a, p. 1411)
Bitar & Espy-Wilson do this by using a knowledge-based eventseeking approach for extracting phonetic features from the
microphone signal on the basis of acoustic cues.
We propose an acoustic-phonetic mapping procedure on the basis
of a Kohonen network.
DATA
Texts
English, German, Italian and Dutch texts from the EUROM0
database, read by 2 male + 2 female speakers per language
DATA
Signals
• 12 mel-frequency cepstral coefficients (MFCC’s)
• energy
• corresponding delta parameters
Hamming window: 15 ms
step size: 5 ms
pre-emphasis: 0.97
16 kHz microphone signals
DATA (1)
Labels
The consonants were transcribed with SAMPA symbols, except:
• plosives and afficates are subdivided into a closure (“p0” =
voiceless closure; “b0” = voiced closure) and a burst-plusaspiration (“p”, “t”, “k”) or frication part (“f”, “s”, “S”, “z”,
“Z”)
• Italian geminates were pooled with non-geminates to prevent
undertraining of geminate consonants
• The Dutch voiced velar fricative [], which only occurs in
some dialects, was pooled with its voiceless counterpart
[x] to prevent undertraining
DATA (2)
Labels
• SAMPA symbols are phonemic within a language, but can
represent different allophones cross-linguistically. These were
relabelled as shown in the table below:
SAMPA allophone label description
language
r

rapr alv. approx.
English
r
ralv alveolar trill
It., Dutch

Ruvu uvular trill
G., Dutch
v

vapr
labiod.
approx. German
v
vfri vd. labiod. fric. E., It., NL
w

vapr labiod. approx. Dutch
w
w
bilab. approx. Engl., It.
SYSTEM ARCHITECTURE
consonant
lexicon
hidden Markov
modelling
language model
phonetic features
Kohonen
network
Kohonen
Kohonen
network
network
MFCC’s + energy
delta parameters
C
CONFUSIONS BASELINE
(by Attilio Erriquez)
phonetic categories:
manner, place, voicing
1 category wrong
2 categories wrong
3 categories wrong
CONFUSIONS MAPPING
(by Attilio Erriquez)
phonetic categories:
manner, place, voicing
1 category wrong
2 categories wrong
3 categories wrong
ACIS =
total of all correct identification percentages
number of consonants to be identified
The Average Correct Identification Score compensates for the
number of occurrences in the database, giving each consonant
equal weight.
It is the total of all percentage numbers along the diagonal of
the confusion matrix divided by the number of consonants.
Baseline system:
31.22 %
Mapping system:
68.47 %
BASELINE SYSTEM
• good identification of language-specific phones
• reason: acoustic homogeneity
• poor identification of other phones
% correct
cons. baseline mapping

100.0
75.0

100.0 100.0

100.0
100.0

97.8
91.3
w
94.1
100.0

91.2
96.5
x
88.2
93.4
language
German
Italian
Italian
English
Engl., It.
English
G, NL
MAPPING SYSTEM
• good identification, also of acoustically variable phones
• reason: variable acoustic parameters are mapped onto
homogenous, distinctive phonetic features
% correct
cons. baseline mapping
h
6.7
E,G, NL

0.0
all
b
0.0
all
d
0.4
all

5.9
language
86.7
58.2
44.0
36.9
38.3
AFFRICATES (1)
cons.
pf
f
s
s


dz
z
d

% correct
baseline mapping language
0.0 100.0
German
1.2
64.4
all
0.0
72.2
German, It.
3.1
64.7
all
0.0
40.2
E., G., It.
78.1
90.6
all
0.0
70.3
Italian
10.4
50.5
all
28.0
96.0
English, It.
no intervocalic realisations
AFFRICATES (2)
• affricates, although restricted to fewer languages, are
recognised poorly in the baseline system
• reason: they are broken up into closure and frication segments,
which are trained separately in the Kohonen networks; these
segments occur in all languages and are acoustically variable,
leading to poor identification
• this is corroborated by the poor identification rates for
fricatives in the baseline system (exception: //, which only
occurs rarely)
• after mapping, both fricatives and affricates are identified well
APMS =
phonetic misidentification coefficient
sum of the misidentification percentages
The Average Phonetic Misidentification Score gives a measure
of the severity of the consonant confusions in terms of phonetic
features.
The multiple is the sum of all products of the misidentification
percentages (in the non-diagonal cells) times the number of
misidentified phonetic categories (manner, place and voicing). It
is divided by the total of all the percentage numbers in the nondiagonal cells.
Baseline system:
1.79
Mapping system:
1.57
APMS =
phonetic misidentification coefficient
sum of the misidentification percentages
• after mapping, incorrectly identified consonant is on average
closer to the phonetic identity of the consonant which was
produced
• reason: the Kohonen network is able to extract linguistically
distinctive phonetic features which allow for a better
separation of the consonants in hidden Markov modelling.
CONSONANT CONFUSIONS
cons. identified as
r
 (61%),  (16%), w
(13%)
BASELINE
j
 (53%), j (18%), 
(12%),
 (6%), r (6%),  (6%)
m
w (23%),  (18%), m
(16%),  (13%),  (10%)
cons. identified as

w (28%),  (18%), 
r
r (84%),  (5%), l (4%)
(16%),
j
j (94%), z (6%)
 (12%), m (8%),  (8%)
m
m (63%),  (11%), 

w (42%),  (15%), 
(10%),
r (6%)
(15%), m (8%),  (8%),  (8%)

 (26%), m (21%),  (20%),
MAPPING
r (6%)

 (46%),  (23%), m (15%),
w (8%)
CONCLUSIONS
Acoustic-phonetic mapping helps to address linguistically
relevant information in the speech signal, ignoring extralinguistic sources of variation.
The advantages of mapping are reflected in the two measures
which we have presented:
• ACIS
shows that mapping leads to better consonant
identification rates for all except a few of the languagespecific consonants. The improvement can be put down to the
system’s ability to map acoustically variable consonant
realisations to more homogeneous phonetic feature vectors.
CONCLUSIONS
Acoustic-phonetic mapping helps to address linguistically
relevant information in the speech signal, ignoring extralinguistic sources of variation.
The advantages of mapping are reflected in the two measures
which we have presented:
• APMS shows that the confusions which occur in the mapping
experiment are less severe than in the baseline experiment
from a phonetic point of view. There are fewer confusions on
the phonetic dimensions manner, place and voicing when
mapping is applied, because the system focuses on distinctive
information in the acoustic signals.
REFERENCES (1)
Bitar, N. & Espy-Wilson, C. (1995a). Speech parameterization based
on phonetic features: application to speech recognition. Proc. 4th
European Conference on Speech Communication and Technology,
1411-1414.
Bitar, N. & Espy-Wilson, C. (1995b). A signal representation of
speech based on phonetic features. Proc. 5th Annual Dual-Use Techn.
and Applications Conf., 310-315.
Kirchhoff, K. (1996). Syllable-level desynchronisation of phonetic
features for speech recognition. Proc. ICSLP., 2274-2276.
Dalsgaard, P. (1992). Phoneme label alignment using acousticphonetic features and Gaussian probability density functions.
Computer Speech and Language 6, 303-329.
REFERENCES (2)
Koreman, J., Barry, W.J. & Andreeva, B. (1997). Relational phonetic
features for consonant identification in a hybrid ASR system.
PHONUS 3, 83-109. Saarbrücken (Germany): Institute of Phonetics,
University of the Saarland.
Koreman, J., Barry, W.J., Andreeva, B. (1998). Exploiting transitions
and focussing on linguistic properties for ASR. Proc ICSLP. (these
proceedings).
SUMMARY
Acoustic-phonetic mapping leads to fewer and
phonetically less severe consonant confusions.
Practical:
THE END