CHAPTER-4 SUBSEGMENTAL, SEGMENTAL AND...
Transcript of CHAPTER-4 SUBSEGMENTAL, SEGMENTAL AND...
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CHAPTER-4 SUBSEGMENTAL, SEGMENTAL AND SUPRASEGMENTAL
FEATURES FOR SPEAKER RECOGNITION USING GAUSSIAN MIXTURE MODEL
Speaker recognition is a pattern recognition task which
involves three phases namely, feature extraction, training and
testing.
In the feature extraction stage, features representing speaker
information are extracted from the speech signal. In the present
study LP residual derived from the speech data is used for training
and testing and also processing of LP residual in time domain at
subsegmental, segmental and suprasegmental levels. In the
training phase, GMMs are built, one for each speaker, using the
training data of the speaker. During the testing phase, the models
are tested with the test data. Based on the results with test data,
decision is made about the identity of the speaker.
4.1 THE SPEECH FEATURE EXTRACTION
The selection of the best parametric representation for acoustic
data is an important task in the design of any text-independent
speaker recognition system. The acoustic features should fulfill the
following requirements.
Be of low dimensionality to allow a reliable estimation of
parameters of the Automatic speaker recognition systems.
Be independent of the speech and recording environment.
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4.1.1. PRE-PROCESSING
The task begins with the pre-processing of the speech signal
collected from each speaker. The speech signal is sampled at 16000
samples/sec and it is resampled to 8000 samples/sec. In the
preprocessing stage, the given speech utterance is pre-emphasized,
blocked into a number of frames and windowed. The frame size
chosen is 5 msec which corresponds 40 samples and a frame shift
between frames is 2.5 msec which corresponds to 20 samples has
been taken in the subsegmental processing of LP residual. The
frame size chosen is 20 msec which corresponds 40 samples and a
frame shift between frames is 2.5 msec which corresponds to 20
samples has been taken in the segmental processing of LP residual
and its sampling frequency is decimated by 4 times hence frame
size is same as subsegmental level. The frame size chosen is 250
ms which corresponds 40 samples and a frame shift between
frames is 6.25 msec which corresponds to 1 sample has been taken
in the suprasegmental processing of LP residual in which signal is
decimated by 50 times. The preprocessing task is carried out in a
sequence of steps as explained below.
4.1.1.1. Pre-Emphasis
The given speech samples in each frame are passed through a
first order filter to spectrally flatten the signal and make it less
susceptible to finite precision effects later in the signal processing
task. The pre-emphasis filter used has the form H (z) =1- z-1, 0.9≤ a ≤1.0. In fact, it is sometimes better to difference the entire speech
utterance before frame blocking and windowing.
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4.1.1.2. Windowing
After pre-emphasis, each frame is windowed using a window
function. The windowing ensures that the signal discontinuities at
the beginning and end of each frame are minimized. The window
function used is the Hamming window given below,
W (n) =0.54- 0.46 , 0≤n≤N-1 (4.1)
Where N is the number of samples in the frame.
4.1.2. Approach to Speech Feature Extraction
One of the early problems in speaker recognition systems was
to choose the right speaker specific excitation source features from
the speech. Excitation source models were chosen to be GMM or
HMM, as they are assumed to offer a good fit to the statistical
nature of speech. Moreover, the excitation source models are often
assumed to have diagonal covariance matrices which arises the
need for speech features those are by nature uncorrelated.
Speaker recognition system uses subsegmental, segmental
and suprasegmental features from LP residual represent different
speaker specific excitation source features. These features are
robust to channel and environmental noise. We present a brief
overview of subsegmental, segmental and suprasegmental features
of LP residual.
4.1.2.1. Subsegmental, Segmental and Suprasegmental Features of the LP Residual
The 12th order LP residual signal is blocked into frames using
specified frame size of 20 msec and frame shift of 10 msec. The LP
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residual contains more speaker specific information. It has been
shown that humans can recognize people by listening to the LP
residual signal [57]. This may be attributed the speaker-specific
excitation source information present at different levels. This work
views the speaker specific excitation source information.
In subsegmental analysis, the LP residual is blocked into
frames of size 5 msec considered in shifts of 2.5 msec for extracting
the dominant speaker information in each frame. Each frame has
40 samples with shift 20 samples in segmental analysis, the LP
residual is blocked into frames of size 20 ms considered in shifts of
2.5 msec for extracting the pitch and energy of the speaker. In
suprasegmental analysis, the LP residual is blocked into frames of
size 250 msec considered in shifts of 6.25 msec for extracting long-
term information, which has very low frequency information of the
speaker. At each level source based speaker characteristics are
represented in the database independently using GMM and
combine them to improve the Speaker recognition system.
4.2 GAUSSIAN MIXTURE MODEL FOR SPEAKER RECOGNITION
GMM is a classic parametric method best used to model
speaker identities due to the fact that Gaussian components have
the capability of representing some general speaker dependent
spectral shapes. Gaussian classifier has been successfully
employed in the several text-independent speaker identification
applications since the approach used by this classifier is similar to
that used by the long term average of spectral features for
representing a speaker’s average vocal tract shape [101].
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In a GMM model, the probability distribution of the observed data
takes the form given by the following equation [102].
(4.2)
Where M is the number of component densities, x is a D
dimensional observed data, bi ( x ) is the component density and pi
is the mixture weight for i = 1, .., M as shown in Fig. 4.1.
= (4.3)
Each component density denotes a D-dimensional
normal distribution with mean vector and covariance matrix ∑i.
The mixture weights satisfy the condition and therefore
represent positive scalar values. These parameters can be
collectively represented as λ = { i} for i = 1,….. M. Each speaker
in a speaker in a speaker identification system can be represented
by one distinct GMM and is referred by the speaker’s models λi, for
i = 1, 2, 3, …N, where N is the number of speakers.
4.2.1. Training the Model
The training procedure is similar to the procedure followed
in vector quantization. Clusters are formed within the training data.
Each cluster is then represented with multiple Gaussian probability
distribution function (pdf). The union of many such Gaussian pdf’s
is a GMM.
The most common approach to estimate the GMM
parameters is the maximum likelihood estimation [103], where
P(X/λ) is maximized with respect to λ. P(X/λ) is the conditional
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probability and vector X = {x1, x2, ….xi} is the set of all feature
vectors belonging to a particular acoustic class. Since there is no
closed form solution to the maximum likelihood estimation,
convergence is guaranteed only when large enough data is
available. An iterative approach for computing the GMM model
parameters using Expectation-maximization (EM) algorithm [104] is
followed.
Fig. 4.1: Diagram of Gaussian Mixture Model.
E-Step: Posterior probabilities are calculated for all the training
feature vectors. Posterior probability for a feature vector i of the nth
frame of the given speaker is follows
P = (4.4)
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M-Step: The M-step uses the posterior probabilities from the E-
Step to estimate model parameters as follows:
(4.5)
i = (4.6)
And
I = (4.7)
Set Pi = , I and i, and iterate the sequence of
E-step and M-step a few times by iteratively checking for the
condition. The EM algorithm improves on the GMM parameter
estimates by iteratively checking for the condition
P (X| z+1) > P (X| z) (4.8)
4.2.2. Testing the Model
Let the number of models representing different acoustic
classes be N. hence j, where j = {1, 2, 3,….N}, is the set of GMMs
under consideration. For each test utterance, feature vectors xn at
time n are extracted. The probability of each model given the
feature vectors xn is given by
P( j|xn) = (4.9)
Since P(xn) is a constant and P( j), the apriori probabilities, are
assumed to be equal, the problem is reduced to finding the j that
maximizes . But is given by
= P({x1,x2,……,xI}| j) (4.10)
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Where, I is the number of feature vectors for each frame of the
speech signal belonging to a particular acoustic class. Assuming
that each frame is statistically independent, Equation 4.10 can be
written as
P({x1,x2,……,xI}| j) = (4.11)
Applying logarithm on Equation 4.9 and simplifying for N we have
Nr= (4.12)
Where Nr is declared as the class to which the feature vectors
belong. Note that {Nr, r = {1,2,3,……,N}} is the set of all acoustic
classes.
4.3 EXPERIMENTAL RESULTS
4.3.1. Database Used for the Study
In this study we consider identification task on TIMIT
Speaker database [4]. The TIMIT corpus of read speech has been
designed to provide speaker data for the acquisition of acoustic-
phonetic knowledge and for the development and evaluation of
automatic speaker recognition systems. TIMIT contains a total of
6300 sentences, 10 sentences spoken by each of 630 speakers from
8 major dialect regions of the United States. We consider 380
utterances spoken by 38 speakers out of 630 speakers for speaker
recognition. For each speaker maximum of 10 speech utterances
among which 8, 7 and 6 are used for training and tested with
minimum 2, 3 and 4 speech utterances.
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4.3.2. Experimental Setup
In general, speaker recognition refers to both speaker
identification and speaker verification. Speaker identification is the
task of identifying a given speaker from a set of speakers. In the
closed-set speaker identification no speaker outside the given set is
used for testing. Speaker verification is the task of verification is
either to accept or reject the claim of the speaker. In this work
experiments have been carried out on closed-set speaker
identification.
The system has been implemented in Matlab7 on Windows
XP platform. We have used LP order of 12 for all experiments. We
have trained the model using Gaussian mixture components as 4,
8, 16 and 32 for different training and testing speech utterances
which are spoken by 38 speakers respectively. Here, recognition
rate is defined as the ratio of the number of speakers identified to
the total number of speakers tested.
4.3.3. Extraction of Complete Source Information of LP Residual, HE of LP Residual and RP of LP Residual at Different levels.
As the envelope of the short-time spectrum corresponds to
the frequency response of the vocal tract shape, one can observe
the short-time spectrum of the LP residual for different LP orders
and the corresponding signal LP spectra to determine the extent of
the vocal tract information present in the LP residual. As the order
of the LP analysis is increased, the LP spectrum approximates the
short-time spectral envelope better. The envelope of the short-time
spectrum corresponds to the frequency response of the vocal tract
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shape, thus reflecting the vocal tract system characteristics.
Typically the vocal tract system is characterized by a maximum of
five resonances in the 0-4 kHz range. Therefore LP order of about 8-
14 seems to be most appropriate for a speech signal resample at 8
kHz. For low order, say 3 as shown in Fig.4.3 (a), the LP spectrum
may pick up only the prominent resonances, and hence the
residual will still have a large amount of information about the
vocal tract system. Thus the spectrum of the residual Fig.4.3 (b)
contains most of the information of the spectral envelope, except for
the prominent resonances. On the other hand, if a large order, say
30 is used, then the LP spectrum may pick up spurious peaks as
shown in Fig. 4.3(e). These spurious peaks influence the
corresponding LP residual obtained by passing the speech signal
through may be affected due to the influence of these spurious
nulls in the spectrum of the inverse filter is shown in Fig 4.2 (f).
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Fig. 4.2: (a) LP Spectrum, (b) LP Residual Spectrum for LP Order 3, (c) LP Spectrum, (d) Residual Spectrum for LP Order 9, (e) LP Spectrum and (f) Residual Spectrum for LP Order 30.
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From above discussion, it is evident that LP residual does
not contain any significant features of the vocal tract shape for LP
orders in the range 8-20. The LP residual may contain mostly the
source information at subsegmental, segmental and
suprasegmental levels. The features derived from the LP residual at
these levels are called as residual features. We verified that the
speaker-specific information present in the LP residual dominates
the amplitude information than phase information due to inverse
filtering. Hence we separate the amplitude information and phase
information of the LP residual using Hilbert transform, hence
amplitude information contained in the HE of LP residual and
phase information contained in RP of LP residual at subsegmental,
segmental and suprasegmental levels are shown in Figs 4.3 and
4.4.
Figs: 4.3 Analytical Signal Representation of a) Subsegmental, b) Segmental and c) Suprasegmental Feature Vectors using HE
of LP Residual.
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Fig 4.4: Subsegmental, Segmental and Suprasegmental Feature Vectors for RP of LP Residual.
4.3.4. Effect of Model at subsegmental, segmental and Suprasegmental Levels and Amount of Training and Test data
This section presents performance of the subsegemental,
segmental and suprasegmental levels of LP residual (complete
source) based speaker recognition systems with respect to number
of mixture components per model (At each level) and amount of
training and testing data. The recognition performance of
subsegmental, segmental and suprasegmental levels of LP residual
for the amount of train and test data is shown in the Tables 4.1 and
4.2.
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4.4 DISCUSSION ON SPEAKER RECOGNITION PERFORMANCE
The speaker recognition performance with respect to
training and testing data is presented in section 4.4.1 with detailed
explanation.
4.4.1. With Respect to Varying Amount of Training and Testing Data
For better performance, we are applying a condition that the
testing utterances are less than training utterances for better
understanding of performance and good authentication, by
following the previous condition the training utterances are
decreased testing utterances are increased till it reach 6 and 4
utterances respectively among the 10 utterances per speaker.
In this experiment, for each speaker to develop one model
with mixture components 2, 4, 8 and 32 by using GMM for training
utterances. The various amounts of training data were sequentially
taken and tested with corresponding testing utterances by following
above conditions.
We observed best results for 6-4 utterance model
compared to 8-2 and 7-3 utterance models, which are discussed in
further sections.
It is also evident that when there is no enough training
data, the selection of model order becomes more important. For all
amounts of training data, performance is increased from 2 to 32
Gaussian components.
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4.5 MODELING SPEAKER INFORMATION FROM SUBSEGMENTAL (Sub), SEGMENTAL (Seg) AND SUPRASEGMENTAL (Supra) LEVELS OF LP RESIDUAL
Speaker-specific excitation source information extracted at
subsegmental level in which one pitch cycle is modeled. At this
level, GMM model is used to capture variety of speaker
characteristics. Blocks of 40 samples from the voiced regions of LP
residual are used as input to the GMM model. Successive blocks
are formed with a shift of 20 samples. One GMM model is trained
using LP residual at subsegmental level. Since the block size is less
than a pitch period, the variety characteristics of the excitation
source (LP residual) within one glottal pulse are captured. The
performance of speaker identification at subsegmental of LP
residual is shown in Figs 4.5(a), 4.7(a) and in the 2nd column of
Tables 4.1 and 4.2. At the segmental level, two to three glottal
cycles of speaker-specific information is modeled in which the
information may be attributed to pitch and energy. At this level,
GMM model is used to capture variety of speaker characteristics.
Blocks of 40 samples from the voiced regions of LP residual are
used as input to the GMM model. Successive blocks are formed
with a shift of 5 samples. One GMM model is trained using LP
residual at segmental level. Since the block size is 2-3 pitch period,
the variety characteristics of the excitation source (LP residual)
within 2-3 glottal pulse is captured The performance of the Speaker
recognition system at segmental level is shown in Figs 4.5(a) ,4.7 (a)
and in the 3rd column of Table 4.1 and 4.2. At the suprasegmental
level, 25 to 50 glottal cycles of speaker-specific information is
modeled in which the information may be attributed to long-term
variations means that using this feature speaker is recognized even
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though the speaker is aged. This is motivation of my work. The
performance of the Speaker recognition system at suprasegmental
level is shown in Figs 4.5(a), 4.7(a) and in the 4th column of Tables
4.1 and 4.2. These are compared with base line Speaker recognition
system using MFCCs in the 6th column of tables 4.1 and 4.2.
For comparison purpose, the base line speaker recognition
system using speaker information from the vocal tract and
segmental source feature are developed for the same database.
Speech signal is processed in blocks of 20 msec with a shift of 10
msec. For every frame 39 dimensional MFCCs are computed. The
performance of this speaker recognition system is shown in figs
4.5(b) and 4. 7(b) and Tables 4.1 and 4.2 and this compared with
the Speaker recognition performance of sub, seg and supra levels of
LP residual.
The performance of speaker recognition system have been given in
the form of percentile(%) in the all the Tables of this chapter.
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Table 4.1: Speaker recognition performance of Subsegmental (Sub), Segmental (Seg) and Suprasegmental (Supra) information of 38 speakers from TIMIT database. Each speaker spoken 10 sentences, among them 7 used as training 3 used as testing.
No. of Mixtur
es
Sub
(%)
Seg
(%)
Supra
(%)
SRC=
Sub+seg+supra
(%)
MFCCs
(%)
Sub+Seg+
Supra+ MFCCs
(%)
2 13.33 20 10 10 30 16.67
4 35 26.67 5 23.33 36.67 30
8 36.67 43.33 5 46.67 46.67 60
16 83 56.67 5 80 66.67 80
32 93.33 56.67 5 83.33 60 83.37
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Fig. 4.5: The Performance of Speaker Recognition System for a) Sub, Seg and Supra Levels of LP Residual and b) Sub+Seg+Supra along with MFCCs
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4.5.1 Combining Evidences From Subsegmantal, Segmental and Suprasegmental Levels of LP Residual
By the way of deriving each feature, the information present
at subsegmental, segmental and suprasegmental levels are different
and hence may reflect different aspect of speaker specific source
information. By comparing their recognition performance it can be
observed that the subsegmental features provide best performance.
Thus the sub segmental features may have more speaker-specific
evidence compared to other level features. The different
performances of the recognition experiments indicate the different
nature of speaker information present.
In case of identification, the muddle pattern of features is
considered as an indication of the different nature of information
present. In the confusion Pattern, principal diagonal represents
correct identification and the rest represents miss classification.
Figure 4.6 shows the confusion patterns of the identification results
conducted for all the proposed features using TIMIT database,
respectively. In each case, the confusion pattern is entirely
different. The decisions for both true and false identification are
different. This indicates that they reflect different aspect of source
information. This may help in combining the evidences for further
improvement of the recognition performance from the complete
source perspective.
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Fig.4.6: Confusion Pattern of a) Sub, b) Seg, c) Supra of LP Residual, d) SRC=Sub+Seg+Supra e) MFCCs and f) SRC+MFCC’s
Information for Identification of 38 Speakers from TIMIT Database.
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Table 4.2: Speaker recognition performance of Sub, Seg and Supra information of 38 speakers from TIMIT database. Each speaker spoken 10 sentences, among them 6 used for training 4 used for testing.
No. of Mixtures
Sub (%)
Seg (%)
Supra (%)
SRC= Sub+seg+supra
(%)
MFCCs (%)
SRC+MFCCs (%)
2 20 13.33 10 26.66 50 36.66
4 43.33 20 13.33 46.66 50 46.66
8 73.33 46.66 10 76.66 50 76.66
16 90 56.66 6.66 86.66 66.67 90
32 96.66 70 3.33 80 70 83.33
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Fig. 4.7: The Performance of Speaker Recognition System for a) Sub, Seg and Supra Levels of LP Residual and b) Sub+Seg+Supra
along with MFCCs.
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4.6 MODELING SPEAKER INFORMATION FROM SUBSEGMENTAL SEGMENTAL AND SUPRASEGMENTAL LEVELS OF HE OF LP RESIDUAL
The amplitude information is obtained from LP residual using
Hilbert transform and which is 900 a shifted version of LP residual.
Since the HE represents the magnitude information of the LP
residual. The HE of LP residual is processed at subsegmental,
segmental and suprasegemental levels. In this subsegmental,
segmental and suprasegemental sequences are derived from the HE
of LP residual is called as HE features. The speaker recognition
performances for subsegmental, segmental and suprasegmental
levels are shown in Figs 4.8(a)-4.10(a) respectively. The combined
amplitude information at each level is improved is shown in Figs
4.8(b)-4.10(b) respectively. The experimental results shown in
Tables 4.3, 4.4 and 4.5 for 38 speakers of TIMIT database.
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Table 4.3: Speaker recognition performance of Sub, Seg and Supra information of HE of LP residual of 38 speakers from TIMIT database. Each speaker spoken 10 sentences, among them 8 used for training 2 used for testing.
No. of mixtures
Sub (%)
Seg (%)
Supra (%)
SRC = Sub+seg+supra
(%)
MFCCs (%)
SRC+MFCCs (%)
2 26.67 46.67 3.33 40 33.33 33.33
4 46.67 30 3.33 60 50 63.33
8 33.33 30 0 50 53.33 56.33
16 50 40 3.33 50 53.33 56.33
32 70 53.33 3.33 63.33 60 64
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Fig.4.8: The Performance of Speaker Recognition System for a)
Sub, Seg and Supra Levels of HE of LP Residual and b) Sub+Seg+Supra along with MFCCs.
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Table 4.4: Speaker recognition performance of Sub, Seg and Supra information of HE of LP residual of 38 speakers from TIMIT database. Each speaker spoken 10 sentences, among them 7 used for training 3 used for testing.
No. of mixtures
Sub (%)
Seg (%)
Supra (%)
SRC=Sub+seg+ Supra
(%)
MFCCs (%)
SRC+MFCCs (%)
2 13.33 6.67 20 13.33 33.33 23.33
4 36.67 26.67 3.33 40 50 40
8 36.67 40 3.33 46.67 53.33 46.67
16 60 43.33 3.33 70 53.33 70
32 70 76.67 6.67 80 66.67 80
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Fig. 4.9: The Performance of Speaker Recognition System for a) Sub, Seg and Supra Levels of HE of LP Residual and b)
Sub+Seg+Supra along with MFCCs.
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Table 4.5: Speaker Recognition Performance of Sub, Seg and Supra Information of HE of LP residual of 38 speakers. Each speaker spoken 10 sentences, among them 6 used for Training 4 used for Testing.
No. of mixtures
Sub (%)
Seg (%)
Supra (%)
SRC=Sub+seg+ Supra
(%)
MFCCs (%)
SRC+MFCCs (%)
2 20 16.67 0 23.33 26.67 50
4 36.67 16.67 3.33 33.33 50 76.67
8 46.67 50 3.33 46.67 56.67 60
16 53.33 53.33 3.33 80 53.33 66.67
32 80 43.33 3.33 66.67 63.33 36.67
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Fig 4.10: The performance of Speaker Recognition System for a) Sub, Seg and Supra Levels of HE of LP Residual and
b) Sub+Seg+Supra along with MFCCs.
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4.7 MODELING SPEAKER INFORMATION FROM SUBSEGMENTAL SEGMENTAL AND SUPRASEGMENTAL LEVELS OF RP OF LP RESIDUAL
The Phase information is obtained from LP residual using
Hilbert transform and which is 900 a shifted version of LP residual.
Since the HE represents the magnitude information of the LP
residual, we can obtain the cosine of the information from LP
residual by dividing with HE. Hence we obtain phase information
from LP residual is known as RP of LP residual. The RP of LP
residual is processed at subsegmental, segmental and
suprasegmental levels. These levels are derived from RP of LP
residual is called as RP features. The speaker recognition
performances for subsegmental, segmental and suprasegmental
levels for RP of LP residual are shown in Figs 4.11(a)-4.13(a)
respectively. The combined phase information at each level is
improved and shown in figs 4.11(b)-4.13(b). The experimental
results shown in tables 4. 6 - 4. 8 for 38 speakers.
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Table 4.6: Speaker recognition performance of Sub, Seg and Supra information of RP of LP residual of 38 speakers. Each speaker spoken 10 sentences, among them 8 used for training 2 used for testing.
No. of mixtur
es
Sub (%)
Seg (%)
Supra (%)
SRC=Sub+seg+ supra (%)
MFCCs (%)
SRC+MFCCs (%)
2 10 20 6.67 43.33 33.33 13.33
4 36.67 23 3.33 26.67 50 63.33
8 30 56 3.33 66.67 53.33 50
16 50 56 3.33 76.67 53.33 76.67
32 80 73.33 3.33 80 60 84.67
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Fig 4.11: The Performance of Speaker Recognition System for
a) Sub, Seg and Supra Levels of RP of LP Residual and b) Sub+Seg+Supra along with MFCCs.
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Table 4.7: Speaker recognition performance of Sub, Seg and Supra information of RP of LP residual of 38 speakers. Each speaker spoken 10 sentences, among them 7 used for training 3 used for testing.
No. of mixtures
Sub (%)
Seg (%)
Supra (%)
SRC=Sub+ seg+supra
(%)
MFCCs (%)
SRC+MFCCs (%)
2 53.33 13.33 0 13.33 33.33 63.33
4 43.33 30 3.33 30 50 60
8 56.67 40 3.33 40 53.33 70
16 66 36.67 3.33 36.67 53.33 70
32 76.67 30 3.33 30 66.67 83.333
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Fig 4.12: The Performance of Speaker Recognition System for a) Sub, Seg and Supra Levels of RP of LP Residual and b) Sub+Seg+Supra along with MFCCs.
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Table 4.8: Speaker recognition performance of Sub, Seg and Supra information of RP of LP residual of 38 speakers from TIMIT database. Each speaker spoken 10 sentences, among them 6 used for training 4 used for testing.
No. of mixtur
es
Sub (%)
Seg (%)
Supra (%)
SRC=Sub+ seg+supra
(%)
MFCCs (%)
SRC+MFCCs (%)
2 40 36.67 0 46.67 26.67 50
4 60 43.33 3.33 70 50 76.67
8 53.33 33.33 3.33 60 56.67 60
16 56.67 40 3.33 66.67 53.33 66.67
32 70 43.33 3.33 36.67 63.33 36.67
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Fig 4.13: The Performance of Speaker Recognition System for a) Sub, Seg and Supra Levels of RP of LP Residual and b) Sub+Seg+Supra along with MFCCs.
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4.8 COMBINING EVIDENCES FROM SUBSEGMENTAL, SEGMENTAL AND SUPRASEGMENTAL LEVELS OF HE AND RP OF LP RESIDUAL
The procedure to compute subsegmental, segmental and
suprasegmental feature vectors from HE and RP of the LP is same
as described earlier except the input sequence. In one case the
input will be HE and the other case it will be RP. The unipolar
nature of the HE helps in suppressing the bipolar variations
representing sequence information and emphasizing only the
amplitude values. As a result, the amplitude information in the
subsegmental, segmental and suprasegmental sequences of LP
residual are shown in Figs 4.3 (a) (b) and (c). On the other hand,
the residual phase represents the sequence information of thee
residual samples. Figs 4.4 (a), (b) and (c) show the residual phase of
the subsegmental, segmental and suprasegmental processing
respectively. In all these cases, the amplitude information is
absent. Hence analytic signal representation provides amplitude
and sequence information of the LP residual samples
independently. In [113] it was shown that information present in
the residual phase significantly contributes to the speaker
recognition. We propose that, information present in the HE may
also contribute well to speaker recognition. Further, as they reflect
different aspect of the source information, the combined
representation of both the evidences may be more effective for
speaker recognition. We conduct different experiments on TIMIT
database for 38 speakers.
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Table 4.9: Speaker recognition performance of Sub, Seg and Supra information of HE and RP of LP residual of 38 speakers. Each speaker spoken 10 sentences, among them 8 used for training 2 used for testing.
No. of mixtures
HE+RP of Sub
(%)
HE+RP of Seg
(%)
HE+RP of
Supra (%)
SRC=HE+Rp of
Sub+seg+supra (%)
MFCCs (%)
SRC+MFCCs (%)
2 13.33 26.67 6.67 20 33.33 23.33
4 60 43.33 6.67 56.67 50 56.67
8 33.33 73.33 33.33 56.67 53.33 60
16 63.33 66.67 46.67 83.33 53.33 86.67
32 66.67 53.33 30 76.67 60 76.67
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Fig 4.14: The Performance of Speaker Recognition System for a) Sub, Seg and Supra levels of HE and RP of LP Residual and b)
Sub+Seg+Supra along with MFCCs.
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Table 4.10: Speaker recognition performance of Sub, Seg and Supra information of HE and RP of LP residual of 38 speakers. Each speaker spoken 10 sentences, among them 7 used for training 3 used for testing.
No. of mixtures
HE+RP of Sub
(%)
HE+RP of Seg
(%)
HE+RP of
Supra (%)
SRC= HE+RP of
Sub+seg+supra (%)
MFCC’s (%)
SRC+MFCC’s (%)
2 20 13.33 20 20 33.33 23.33
4 46.67 30 6.67 46.67 50 46.67
8 53.33 60 3.33 66.67 53.33 70
16 73.33 73.33 6.67 70 53.33 76.67
32 90 75 6.67 86.67 66.67 93.37
100
Fig 4.15: The Performance of Speaker Recognition System for a) Sub, Seg and Supra Levels of HE and RP of LP Residual and
b) Sub+Seg+Supra along with MFCCs.
101
Table 4.11: Speaker recognition performance of Sub, Seg and Supra information of HE and RP of LP residual for 38 speakers. Each speaker spoken 10 sentences, among them 6 used for training 4 used for testing.
No. of mixtures
Sub (%)
Seg (%)
Supra (%)
SRC=Sub+ seg+supra
(%)
MFCCs
(%)
SRC+MFCCs (%)
2 26.67 36.67 6.67 26.67 26.67 50
4 50 43.33 3.33 60 50 76.67
8 60 33.33 6.67 63.33 56.67 60
16 83.33 40 3.33 83.33 53.33 86.67
32 93.33 43.33 3.33 93.33 63.33 98.67
102
Fig 4.16: The Performance of Speaker Recognition System for a) Sub, Seg and Supra Levels of HE and RP of LP Residual and
b) Sub+Seg+Supra along with MFCCs.
103
4.9 Discussion on Speaker Recognition Performance with respect to varying amount of Training and Testing data.
In this experiment, speaker models with 2, 4, 8, 16 and 32
component densities were trained using 8 and 6 speech utterances
and tested with 2 and 4 speech utterances per speaker. The
recognition performance of residual features, HE and RP features
for 2 and 4 test speech utterances versus 8 and 6 speech
utterances are train data are shown in the figs 4.5-4.16 and Tables
4.1-4.11
It is shown that with increase in test speech utterances per
speaker the recognition performance increases. The largest increase
in percentage of recognition for training speech utterances per
speaker, when the amount of test speech utterances are 4 in the
case of residual features, HE and RP features individually shown in
the Tables 4.1-4.8 and Figs 4.5-4.13 and fusion of HE and RP
features since fusion of both provides complete source information
[Tables 4.9-4.11 and Figs 4.14-4.16].
4.10 DISCUSSION ON SPEAKER RECOGNITION PERFORMANCE WITH RESPECT TO DIFFERENT TYPES OF FEATURES
To investigate Speaker recognition performance using LP
residual at subsegmental, segmental and suprasegmental levels
with respect to the component densities per model where each
speaker is model at subsegmental, segmental and suprasegmental
information of LP residual. The performance of subsegmental level
is more than the other two levels. Similarly each speaker is modeled
at subsegmental, segmental and suprasegmental of HE and RP of
104
LP residual. Individually, the performance of HE and RP is less than
residual features. Fusion of HE and RP improves the performance
of Speaker recognition system [Tables and Figs]. Therefore, the
fusion of HE and RP features provides better performance than the
residual features alone.
This shows the robustness of the combined HE and RP
representation of complete source is providing additional
information to the MFCC features. From this observation we
conclude that combined representation of HE and RP features are
better than the residual features alone. It indicates complete
information present in the source can be represented by the
combined representation of the HE and RP features.
4.11 COMPARATIVE STUDY OF HE FEATURES AND RP FEATURES OVER RESIDUAL FEATURES FOR RECOGNITION SYSTEM
We have compared the results obtained by the observed new
approach with some recent works which were discussed in detail in
section 2.6. In these works features used and database are
different. Tables 4.12 and 4.13 shows comparative analysis of
different features for speaker recognition performance
Table 4.12: Comparison of Speaker Recognition Performance at Different Databases for LP residual at Sub, Seg and Supra levels. Database Sub
(%) Seg (%)
Supra (%)
SRC=Sub+ Seg+Supra (%)
MFCCs (%)
SRC+MFCCs (%)
NIST-99 64 60 31 76 87 96
NIST-03 57 58 13 67 66 79
TIMIT 90 56.67 13.33 86.67 66.67 90
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Table 4.13: Comparison of speaker recognition performance at different Databases for HE and RP of LP residual at subsegmental, segmental and suprasegmental levels.
Databases
Type of
signal
(%)
Sub
(%)
Seg
(%)
Supra
(%)
SRC=Sub+Seg+S
upra
(%)
MFCCs
(%)
SRC+MFCCs
(%)
NIST-99
HE 44 56 8 71 87 94
RP 49 69 17 73 87 93
HE+RP 64 78 22 88 87 98
NIST-03
HE 32 39 7 54 66 76
RP 23 51 14 56 66 77
HE+RP 48 59 17 72 66 83
OBSERVEDMODEL
GMM using
Database is
TIMIT
HE 80 76.67 20 80 66.67 80
RP 80 76.67 6.67 80 63.33 84.67
HE+RP 93.33 75 46.67 93.33 63.33 98.67
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4.12 SUMMARY In this chapter, model the speaker-specific source information from
LP residual at subsegmental, segmental and suprasegmental using
GMM. The segmental and suprasegmental level information is
decimated by a factor of 4 and 50, respectively. Experimental
results show that subsegmental, segmental and suprasegmental
levels contain speaker information. Further combining the
evidences from each level, the performance improvement indicates
the different nature speaker information at each level. Towards
the end, the idea of subsegmental, segmental and suprasegmental
features of LP residual, HE of LP residual and RP of LP residual at
subsegmental, segmental and suprasegmental levels for speaker
recognition system using GMM’s are proposed.