Speaker Identification Using Wavelet and Feed Forward
Neural Network
ADZNAN B. J., M.A ALMASHRGY, ELSADIG AHMED, ABD RAHMAN RAMLI
Department of Computer and Communication Systems Engineering/ UPM
Faculty of Engineering University Putra Malaysia
43400UPM, Serdang, Selangor DE
MALAYSIA
Abstract: - This paper carried out Wavelet Packet Transform (WPT) - based feature extraction technique. The
WPT was used to decompose speech signals into sub bands, similar to Mel scale. It takes the Mel Frequency
Cepstral Coefficients (MFCC) for each band as a feature of the speech signal, which is used to differentiate
between speakers. These features have to go for classification. Feed Forward Neural Network (FFNN) is
proposed for the classification task. Instead of using one NN for all speakers, we provide separate NNs for
each speaker. This system is applied on text-dependent speaker recognition in a noisy environment.
Experiments are conducted on ten speakers and they gave considerable results. These yield a very promising
accuracy of about 89.9%.
Key-Words: - Wavelet Packet Transform (WPT), Feed Forward Neural Network (FFNN), Text Dependant
speaker recognition, Speaker Identification Recognition, False Identification Error (FIE), Discrete Cosine
Transform (DWT).
1 Introduction
Speaker recognition is the process of automatically
recognizing who is speaking on the basis of
individual information inside a speech waves. The
goal of speaker recognition system is to extract,
characterize and recognize the information in the
speech signal, conveying speaker identity [1].
The problem of speaker recognition can be
divided into two sub problems: the speaker
identification
and
verification.
Speaker
identification is the task of determining who is
speaking from a set of unknown voices or
speakers. The unknown person makes no identity
claim. Speaker verification is the task of
determining who a person (he/she) claims to be
(yes/no decision) [1].
An error that can occur in speaker
identification is the false identification of speaker
and the error in speaker verification can be
classified into two types: (1) false rejection: true
speaker is rejected and considered an impostor,
and (2) false acceptance: a false speaker is
accepted as a true one [2].
Speaker recognition can be divided into two: textdependent and text-independent methods. In case of
text-dependent method, a speaker is required to utter
a predetermined set of words or sentence. The
features are extracted from the utterance. In case of
text independent method, there is no predetermined
set of words or sentences and the speakers may not
even be aware that they are being tested [3].
All speaker recognition systems are divided into
two parts. First, is extraction and the second is
classification or modeling of speaker based on the
extracted features. Conventional speaker recognition
is performed by recognizing an extracted set of
acoustic feature vectors, which are calculated over
the whole frequency band of input speeches (full
band technique). There are many methods which
have been used for extraction feature from speech
signals. For instance, frequency band analysis,
formant
frequencies,
pitches
contours,
coarticulation, short term processing, linear
prediction, and mel-wraped cepstrum. For more
details refer to [3], [4].
Wavelet and time frequency methods have been
shown to be effective signal processing techniques
over the last two decades for a variety of problems.
In particular, wavelets have been successfully
applied to de-noising tasks and as robust features
[5].
There has been recent interest to use wavelet in
speech recognition. [6, 7] use a wavelet on speech
recognition, and DWT for decomposing the speech
signal.
Instead of dealing with DWT, this paper proposes
to split the signal into sub-bands similar to Mel scale
using WPT. The advantage of using WPT is that it
can segment the frequency axis and has uniform
translation in time [8].
A number of modeling techniques have been
applied to speaker recognition including dynamic
time warping, nearest neighbor, hidden Markov
modeling, Vector quantization, neural network, etc.
In this paper we propose to use Feed Forward neural
Network (FFNN).
The most previous work is concentrated on a
phoneme recognition using 32ms framing. Isolated
word speaker recognition is proposed here to apply
on whole signal, “no framing is conducted on the
speech signal”.
2 Wavelet Transform
Wavelet analysis is a relatively new mathematical
discipline. Wavelets have the ability to analysis
different parts of a signal at different scales.
Wavelet transform provides a variable timefrequncy representation of signals [9].
Fourier transform gives the spectral content of
the signal, but it gives no information regarding
where in time those spectral components appear.
Therefore it is not suitable for non-stationary
signals “such as speech” analysis.
In STFT, the signal is divided into small
segments where the segments of the signal can be
assumed to be stationary. In this method, narrow
windows give good time resolution, but poor
frequency resolution. On the other hand, wide
windows give good frequency resolution, but poor
time resolution.
The wavelet transform solves the dilemma of
the resolution to a certain extent. Wavelet
transform is designed to give good time resolution
and poor frequency resolution at high frequencies
and good frequency resolution and poor time
resolution at low frequencies.
Fig.1 Full decomposition wavelet packet tree
Figure 1 demonstrates a three level wavelet packet
decomposition of a discrete input signal using
appropriate H and G low pass and high pass filters,
respectively. Each H and G filter output, followed
by down sampling by 2, is a wavelet packet. The
wavelet packet at certain decomposition level has
the same scale but different time translation [11].
3 Materials and Methodology
3.1 Experiment Setup
All data set are recorded with microphone in
laboratory environment using 16 kHz sampling
rate. The data consist of 10 speakers, 5 of which
are male and the other female. The speakers
speaking English word “ENTER”. For each
speaker, 5 signals are used for training. For testing
the system 15-25 of other recorded signals are
used.
3.2 End point detection
Due to the noise, which occurred at the start and
the end of the speech signal ‘silence’ the
boundaries ‘start and end’ of the speech signal
must be detected and then remove the silence part.
End Point Detection is used to detect the
boundaries. Figure 2 shows the speech signal
before applying EPD algorithm and the speech
signal after removing the silence ‘noise’
2.1 Wavelet Packet Transform
The DWT is really a subset of the Wavelet Packet
Transform. Wavelet packet [10] decomposition is a
generalization of the discrete wavelet transform
(DWT) and of the multi resolution analysis.
The wavelet packet decomposition (WPD) of a
signal can be viewed as a step by step transformation
of the signal from time domain to frequency domain.
The top level of WPD tree is the time representation
of the signal. As each level of the tree is traversed,
there is an increase in the trade off between time and
frequency resolution. The bottom level of a fully
decomposed tree is a frequency representation of the
signal.
0.3
0.3
0.2
0.2
0.1
0.1
0
0
-0.1
-0.1
-0.2
-0.2
-0.3
-0.3
-0.4
0
1
2
3
-0.4
0
5000
10000
15000
4
x 10
Fig. 2 End Point Detection
3.3 Feature Extraction
Figure 3 describes a block diagram of the feature
extraction task. First, WPT is used to decompose
the speech signal into sub bands. After that some
bands are chosen and the log energy of chosen
band taken. Finally, the DCT for chosen bands are
taken.
Speech
signal
WPT
24
Features
Fifth, applying two levels WP decomposition on
terminal node 9 band which producing four bands.
Sixth, WP decomposes band 10 once. So, two bands
come out.
Log Energy
Bands
DCT
Fig. 4 Tree of the decomposed signal.
Fig. 3 Block diagram of feature extraction
stage
The WPT is used for partitioning the
frequency axis of the speech signal similar to the
Mel scale. The signal is split into sub band
according to [5].
3.3.1 Decomposing the Speech Signal
The second step is to analysis this signal using
wavelet packet transforms. There are many
wavelet transform families, which are used for
wavelet transform. The debouche (db) family
wavelet transforms is chosen for this application.
The decomposition applies on this signal as
follow:
Finally, the four remaining bands are not further
decomposed.
Therefore the final number of bands produced by
WP is 24 bands. From the previous steps we
generate 24 bands. The first 20 bands are taken and
then go for further analysis, which is described in the
following sections.
3.3.2 Log Energy bands calculation
The energy of each band is calculated by taking the
sum of the square value of the coefficients of that
band.
i n
E i   c( n )
2
(1)
Fist, a full three level decomposition is carried out.
The following figure ‘Figure 4’ shows the complete
3 level decomposition tree of the signal. The figure
shows the decomposition three on the left side and
the signal at appropriate node on the right side (in
this figure node 7).
Where:
n= number of coefficients in the band.
c (n)=the wavelet coefficients.
Then take the log of each energy band.
Secondly, further decompose the lowest band by
applying again full three level WP decompositions.
The lowest band is the node number 7 which also
can be represented as (3, 0) node. So, this
decomposition produces 8 sub-bands.
3.3.3 Compute the cosine transforms
Take the discrete cosine transform (dct) for each
chosen band,
The following equation describes the
extracted feature [4]:
Thirdly, the frequency band of node 8 further
decomposes by applying two levels WP
decomposition, thereby giving four sub-bands.
L
 n(i  0.5) 
coef   log E i cos
 , n  1,.., L (2)
L


i 1
Forthly, the two lowest bands of the previous
decomposition further decomposed into two levels
WP decomposition. Therefore the number of bands
which are produced by the third and the forth are six.
Now, the features for each speaker are calculated.
The first part of the recognition is done.Sstill how
do we classify who is speaking. The following
section describes the method, which has been
i 1
Input 1
Input 2
applied in this paper.
The output
Input 3
3.4 Classification
Our approach in the classification paradigm is to
apply FFNN. Instead of using one NN for all
speakers one NN is created for each speaker. Each
NN is designed to make one decision as to
whether or not the test utterance is the target
speaker for that model or not. Whilst it is feasible
for one network to be capable of deciding between
multiple targets, for particular reasons that
approach is not adopted in order to contain the
degree of complexity to a manageable level. The
idea behind this is, if either one of the target
speakers is changed or a new target added, this
would require the network to be retrained on all
speaker, then a new network needs to be
developed. For a new speaker that we want to add
to the group, there will be no need to train the NN.
Just makes a new NN for the coming one. Figure
5 describes the proposed NN diagram.
Input 4
Hidden layer 2
Input 20
Hidden layer 1
Fig.6 NN structure for classification
We trained all the networks using five signals. For
these networks the output target is assigned to 0.5. In
the testing we use a certain threshold, which is
calculated as following:
d  Y  0.5
(3)
Where:
Y: the output of the network.
d: deviation from the target output (0.5).
Net1
Inputs
Net2
Output
Net3
NetN
Fig. 5 A Simple diagram of the proposed
NN.
The NN contains 20 inputs “coefficients”, two
hidden layer, and one output. The first hidden
layers consist of a number of weights and the
second one consists of three weights. And one
output for the each NN. Figure 6 describe the
structure of the NN used in this paper.
For each speaker we use five speech signals to train
its network. After training the network, 15-25 speech
signals for each speaker are used to test the system.
We check the deviation for all speakers. If the
deviation of all speakers is greater than the
threshold, the person who is trying to access the
system is considered an impostor. If not, then the
speaker who was trying to access the system must be
the one who has the small deviation.
4 Experimental Results
Our experiments were conducted on 10 speakers.
For each speaker 20 signals of the English word
“Enter” are recorded. Five of these signals are used
for training the system and the other 15-25 signals
used for testing the system. False Identification Error
(FIE) is calculated for each speaker. FIE is presented
in Table 1.
Table 1 shows the false identification error, which is
calculated for ten speakers using our approach. The
minimum error obtained is 0% for speaker #2. On
the other hand, the maximum error is obtained from
speaker # 5 as 16%. The errors for the other
speakers are between 0-16 %.
From these results the average identification
accuracy for the 10 speakers is 89.9%. In [7] the
average accuracy was 81%, which used the DWT for
extracting the feature from the speech signal and the
Adaptive Resonance Theory (ART) family of neural
network in classifying the extracted feature.
Speaker number
Speaker 1
Speaker 2
Speaker 3
Speaker 4
Speaker 5
Speaker 6
Speaker 7
Speaker 8
Speaker 9
Speaker 10
% of false
identification
error
6.66%
0%
6.66%
0%
16%
13%
16%
10%
16%
6.66%
Table 1 False identification errors
5 Conclusion
In this paper we introduced a feature extraction
method using WPT. The WPT is applied on whole
speech signal, ‘no framing’. This approach gives a
good result for text-dependent speaker recognition.
In some case it gives 0% false rejection error. But
if the number of speaker is increased, it may cause
an increase in the false identification error.
However this system has to be applied for small
number of speakers rather than in large numbers.
On the other hand using the WPT shows an
improved result over the DWT.
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