B.Tech Project Report

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BABU BANARSI DAS INSTITUTE OF TECHNOLOGY, GHAZIABAD
(AFFILIATED TO U.P. TECHNICAL UNIVERSITY, LUCKNOW)
(Established in 2000)
“REAL TIME SPEAKER RECOGNITION”
A project report submitted in partial fulfillment of the requirement for the
Award of Degree of
Bachelor of Technology
In
Electronics and Communication Engineering
Academic session 2008-2012
Submitted By: Project guides:
Rohit Singh (0803531072) Mr. Prashant Sharma
Harish Kumar (0803531409) Mr. Anil Kumar Pandey
Samreen Zehra (0803531074)
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
MAY 2012

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CERTIFICATE
This is to certify that Rohit Singh, Harish Kumar and Samreen Zehra of Department of
Electronics and Communication Engineering of this institute have carried out together the
project work presented in this report entitled „Real Time Speaker Recognition‟ in partial
fulfillment of award of degree of Bachelor of Technology in Electronics and Communication
Engineering from UPTU, Lucknow under our supervision. The report embodies results of
their works and studies carried out by the students themselves and the content of report does
not form the part of any other degree to these candidates or to anybody else.
Project guides:
Mr. Prashant Sharma (AP, ECE)
Mr. Anil Kumar Pandey (AP, ECE)

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ACKNOWLEDGEMENT
We have taken efforts in this project. However, it would not have been possible without the
kind support and help of many individuals. We would like to extend our sincere thanks to all
of them.
We are highly indebted to Mr. K. L. Pursnani, Head of Department Electronic and
Communication Engineering, BBDIT, Ghaziabad, for his guidance and constant supervision
as well as for providing necessary information regarding the project & for his support in
completing the project.
We would like to express our special gratitude and thanks to our project guides, Mr. Prashant
Sharma and Mr. Anil Kumar Pandey for giving us such attention and time. Both of our guides
had taken great pain and efforts to help us the best way without whom this project would ever
be realized.
We would like to express our gratitude towards our parents and friends for their kind co-
operation and encouragement which help us in completion of this project.
Our thanks and appreciations also go to our college in developing the project and people who
have willingly helped us out with their abilities.
Rohit Singh (0803531072)
Harish Kumar (0803531409)
Samreen Zehra (0803531074)

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CONTENTS
1. Introduction…………………………………………………………………………...7
1.1 Project overview……………………………..…………………....………..……..7
1.2 Application…………………...………………………………...………………...10
2. Methodology……………...………………………………………….………………11
2.1 Algorithm………………………………………………...………………………11
2.2 Flow Chart……………………………………………...………………………...12
3. Identification background……………………………………………………….….13
3.1 DSP Fundamentals………………………………………………...……………..13
3.1.1 Basic Definitions………...………………………………….………...…..13
3.1.2 Convolution………………………………………………..……………...14
3.1.3 Discrete Fourier Transform………………………………...……………..14
3.2 Human Speech Production Model…………………………………...…………...15
3.2.1 Anatomy..………………………………………………….……………...15
3.2.2 Vocal Model……………………………………….…………………..….17
3.3 Speaker Identification……………………………………...……………………..18
4. Speech Feature Extraction……………………………………………………….…21
4.1 Introduction…………………………………………………………………...…..21
4.2 Short term analysis…..……………………………………………...…………....22
4.3 Cepstrum…………………………………………………………………...…….23
4.3.1 Delta Cepstrum……………..……………………………………….……24
4.4 Mel-Frequency Cepstrum Coefficients……………………………………….......25
4.4.1 Computing of mel cepstrum coefficient……………………………..……27
4.5 Framing and Windowing………..............................................................................29
5. Vector Quantization……………………………………………..………………......30
6. K-means………………………………………………………………………….…..33
6.1 Mean shift clustering…….…………………………………………...……...…...35
6.2 Bilateral filtering…….………………………………………………………........36
6.3 Speaker matching…………………..……………………………………..……….36
7. Euclidean distance………………………………………………………………...…...38
7.1One dimension…………………………………………………………...……….40
7.2Two dimension…………………….…………………………………..…………40

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7.3Three dimension………….………………………………………………………..40
7.4 N dimensions………………………………………………………………….......40
7.5 Squared euclidean distance …………………....……………………...………….40
7.6 Decision making……………………………………………………...…………..41
8 Result and Conclusion…………………………………………………………………..42
8.1Result…………………………………………………………………………......42
8.2 Pruning Basis…………………...…………………………………………...…...42
8.2.1 Static Pruning…………………………………………………………43
8.2.2 Adaptive Pruning……………………………………………………..44
8.3Conclusion…………………………………………………………….....……….44

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LIST OF FIGURES
Name of Figure Page No
1.1 Identification Taxonomy………………………………………………………………..….8
3.1 Vocal Tract Model…………………………………………………………………….….16
3.2 Multi tube Lossless Model…………………...…………………………………..……….17
3.3 Source Filter Model……………………………………….………………….…………..18
3.4 Enrollment Phase…………………………………………………………….…………...19
3.5 Identification Phase………………………………………………………..……………...20
4.1 Short Term Analysis……………………………………………………….……………..23
4.2 Speech Magnitude Spectrum…………………………………………….……………….25
4.3 Cepstrum………………………………………………………………………….………25
4.4 Computing of mel-cepstrum coefficient……………………………………….…………27
4.5 Triangular Filter used to compute mel-cepstrum ………………………………..……….28
4.6 Quasi Stationary………………………………………………………………..…………29
5.1 Vector Quantization of Two Speakers………………………………………..…………..31
7.1 Decision Process…………………………………………………………….……………41
8.1 Distance stabilization……………………………………………………….…………….42
8.2 Evaluation of static variant using different pruning intervals…………………..………...43
8.3 Examples of the matching score distribution………………………………..……………44

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CHAPTER 1
INTRODUCTION
1.1 Project overview
The human speech conveys different types of information. The primary type is the meaning or
words, which speaker tries to pass to the listener. But the other types that are also included in
the speech are information about language being spoken, speaker emotions, gender and
identity of the speaker. The goal of automatic speaker recognition is to extract, characterize
and recognize the information about speaker identity. Speaker recognition is usually divided
into two different branches:
 Speaker verification
 Speaker identification.
Speaker verification task is to verify the claimed identity of person from his voice. This
process involves only binary decision about claimed identity. In speaker identification there is
no identity claim and the system decides who the speaking person is.
Speaker identification can be further divided into two branches. Open-set speaker
identification decides to whom of the registered speakers‟ unknown speech sample belongs or
makes a conclusion that the speech sample is unknown. In this work,
we deal with the closed-set speaker identification, which is a decision making process of who
of the registered speakers is most likely the author of the unknown speech sample. Depending
on the algorithm used for the identification, the task can also be divided into text-dependent
and text-independent identification. The difference is that in the first case the system knows
the text spoken by the person while in the second case the system must be able to recognize
the speaker from any text. This taxonomy is
Represented in Figure 1.1

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Speaker Recognition
Speaker verification Speaker identification
Closed set Identification Open set Identification
Text independent identification Text dependent identification
Fig. 1.1 Identification Taxonomy
Speaker recognition is basically divided into two-classification: speaker recognition and
speaker identification and it is the method of automatically identify who is speaking on the
basis of individual information integrated in speech waves. Speaker recognition is widely
applicable in use of speaker‟s voice to verify their identity and control access to services such
as banking by telephone, database access services, voice dialing telephone shopping,
information services, voice mail, security control for secret information areas, and remote
access to computer AT and T and TI with Sprint have started field tests and actual
application of speaker recognition technology; many customers are already being used by
Sprint‟s Voice Phone Card.

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Speaker recognition technology is the most potential technology to create new services that
will make our everyday lives more secured. Another important application of speaker
recognition technology is for forensic purposes. Speaker recognition has been seen an
appealing research field for the last decades which still yields a number of unsolved problems.
The main aim of this project is speaker identification, which consists of comparing a speech
signal from an unknown speaker to a database of known speaker. The system can recognize
the speaker, which has been trained with a number of speakers. Below figure shows the
fundamental formation of speaker identification and verification systems. Where the speaker
identification is the process of determining which registered speaker provides a given
speech. On the other hand, speaker verification is the process of rejecting or accepting the
identity claim of a speaker. In most of the applications, voice is use as the key to confirm the
identities of a speaker is classified as speaker verification.
Adding the open set identification case in which a reference model for an unknown speaker
may not exist can also modify above formation of speaker identification and verification
system. This is usually the case in forensic application. In this circumstances, an added
decision alternative, the unknown does not match any of the models, is required. Other
threshold examination can be used in both verification and identification process to decide
if the match is close enough to acknowledge the decision or if more speech data are
needed.
Speaker recognition can also divide into two methods, text-dependent and text independent
methods. In text dependent method the speaker has to say key words or sentences having the
same text for both training and recognition trials. Whereas in the text independent does not
rely on a specific text being speaks. Formerly text dependent methods were widely in
application, but later text independent is in use. Both text dependent and text independent
methods share a problem however.
By playing back the recorded voice of registered speakers this system can be easily deceived.
There are different technique is used to cope up with such problems. Such as a small set of
words or digits are used as input and each user is provoked to thorough a specified
sequence of key words that is randomly selected every time the system is used. Still this
method is not completely reliable. This method can be deceived with the highly developed
electronics recording system that can repeat secrete key words in a request order. Practical

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applications for automatic speaker identification are obviously various kinds of security
systems. Human voice can serve as a key for any security objects, and it is not so easy in
general to lose or forget it. Another important property of speech is that it can be transmitted
by telephone channel, for example. This provides an ability to automatically identify speakers
and provide access to security objects by telephone. Nowadays, this approach begins to be
used for telephone credit card purchases and bank transactions. Human voice can also be used
to prove identity during access to any physical facilities by storing speaker model in a small
chip, which can be used as an access tag, and used instead of a pin code. Another important
application for speaker identification is to monitor people by their voices. For instance, it is
useful in information retrieval by speaker indexing of some recorded debates or news, and
then retrieving speech only for interesting speakers. It can also be used to monitor criminals in
common places by identifying them by voices. In fact, all these examples are actually
examples of real time systems. For any identification system to be useful in practice, the time
response, or time spent on the identification should be minimized. Growing size of speaker
database is its major limitation.
1.2 Application
Practical applications for automatic speaker identification are obviously various kinds of
security systems. Human voice can serve as a key for any security objects, and it is not so
easy in general to lose or forget it. Another important property of speech is that it can be
transmitted by telephone channel, for example. This provides an ability to automatically
identify speakers and provide access to security objects by telephone. Nowadays, this
approach begins to be used for telephone credit card purchases and bank transactions. Human
voice can also be used to prove identity during access to any physical facilities by storing
speaker model in a small chip, which can be used as an access tag, and used instead of a pin
code. Another important application for speaker identification is to monitor people by their
voices. For instance, it is useful in information retrieval by speaker indexing of some recorded
debates or news, and then retrieving speech only for interesting speakers. It can also be used
to monitor criminals in common places by identifying them by voices.
In fact, all these examples are actually examples of real time systems. For any identification
system to be useful in practice, the time response, or time spent on the identification should be
minimized. Growing size of speaker database is its major limitation.

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CHAPTER 2
METHODOLOGY
2.1 Algorithm
1. Record the test sound through microphone.
2. Convert the recorded sound into .wav format.
3. Load recorded sound files from database.
4. Extract features from test file for recognition.
5. Extract features from all the files stored in the database.
6. Find out the centroids of the test file by any clustering algorithm.
7. Find out the centroids of the sample files stored in database so that codebooks can be
generated.
8. Calculate the Euclidean distance between the test file and individual samples of the
database.
9. Find out the sample having minimum distance with the test file.
10. The sample corresponding to the minimum distance is most likely the author of the test
sound.

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CHAPTER 3
IDENTIFICATION BACKGROUND
In this chapter we discuss theoretical background for speaker identification. We start from the
digital signal processing theory. Then we move to the anatomy of human voice production
organs and discuss the basic properties of the human speech production mechanism and
techniques for its modeling. This model will be used in the next chapter when we will discuss
techniques for the extraction of the speaker characteristics from the speech signal.
3.1 DSP Fundamentals
According to its abbreviation, Digital Signal Processing (DSP) is a part of computer science,
which operates with special kind of data – signals. In most cases, these signals are obtained
from various sensors, such as microphone or camera. DSP is the mathematics, mixed with the
algorithms and special techniques used to manipulate with these signals, converted to the
digital form.
3.1.1 Basic Definitions
By signal we mean here a relation of how one parameter is related to another parameter. One
of these parameters is called independent parameter (usually it is time), and the other one is
called dependent, and represents what we are measuring. Since both of these parameters
belong to the continuous range of values, we call such signal continuous signal. When
continuous signal is passed through an Analog-To-Digital converter (ADC)it is said to be
discrete or digitized signal. Conversion works in the following way: every time period, which
occurs with frequency, called sampling frequency, signal value is taken and quantized, by
selecting an appropriate value from the range of7possible values. This range is called
quantization precision and usually represented as an amount of bits available to store signal
value. Based on the sampling theorem, proved by Nyquist in 1940, digital signal can contain
frequency components only up to one half of the sampling rate. Generally, continuous signals
are what we have in nature while discrete signals exist mostly inside computers. Signals that
use time as the independent parameter are said to be in the time domain, while signals that use

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frequency as the independent parameter are said to be in the frequency domain. One of the
important definitions used in DSP is the definition of linear system. By system we mean here
any process that produces output signal in response on a given input signal. A system is called
linear if it satisfies the following three properties: homogeneity, additivity and shift
invariance.
3.1.2 Convolution
An impulse is a signal composed of all zeros except one non-zero point. Every signal can be
decomposed into a group of impulses, each of them then passed through a linear system and
the resulting output components are synthesized or added together. The resulting signal is
exactly the same as obtained by passing the original signal through the system. Every impulse
can be represented as a shifted and scaled delta function, which is a normalized impulse, that
is, sample number zero has a value of one and all other samples have a value of zero. When
the delta function is passed through a linear system, its output is called impulse response. If
two systems are different they will have different impulse responses. Scaled and shifted
impulse response and scaling and shifting of the input are identical to the scaling and shifting
of the output. It means that knowing systems impulse response we know everything about the
system. Convolution is a formal mathematical operation, which is used to describe
Relationship between three signals of interest: input and output signals, and the impulse
response of the system. It is usually said that the output signal is the input signal convolved
with the system‟s impulse response. Mathematics behind the convolution does not restrict
how long the impulse response is. It only says that the size of the output signal is the size of
the input signal plus the size of the impulse response minus one. Convolution is very
important concept in DSP. Based on the properties of linear systems it provides the way of
combining two signals to form a third signal. A lot of mathematics behind the DSP is based
on the convolution.
3.1.3 Discrete Fourier Transform
Fourier transform belongs to the family of linear transforms widely used in DSP based on
decomposing signal into sinusoids (sine and cosine waves).Usually in DSP we use the
Discrete Fourier Transform (DFT), a special kind of Fourier transform used to deal with a
periodic discrete signals. Actually there are an infinite number of ways how signal can be

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decomposed but sinusoids are selected because of their sinusoidal fidelity that means that
sinusoidal input to the linear system will produce sinusoidal output, only the amplitude and
phase may change, frequency and shape remain the same.
3.2 Human Speech Production Model
Undoubtedly, ability to speak is the most important way for humans to communicate between
each other. Speech conveys various kind of information, which is essentially the meaning of
information speaking person wants to impart, individual information representing speaker and
also some emotional filling. Speech production begins with the initial formalization of the
idea which speaker wants to impart to the listener. Then speaker converts this idea into the
appropriate order of words and phrases according to the language. Finally, his brain produces
motor nerve commands, which move the vocal organs in an appropriate way. Understanding
of how human produce sounds forms the basis of speaker identification.
3.2.1 Anatomy
The sound is an acoustic pressure formed of compressions and rarefactions of air molecules
that originate from movements of human anatomical structures. Most important components
of the human speech production system are the lungs (source of air during speech), trachea
(windpipe), larynx or its most important part vocal cords (organ of voice production), nasal
cavity (nose), soft palate or velum (allows passage of air through the nasal cavity), hard
palate (enables consonant articulation), tongue, teeth and lips. All these components, called
articulators by speech scientists, move to different positions to produce various sounds. Based
on their production, speech sounds can also be divided into consonants and voiced and
unvoiced vowels.
From the technical point of view, it is more useful to think about speech production system in
terms of acoustic filtering operations that affect the air going from the lungs. There are three
main cavities that comprise the main acoustic filter. According to they are nasal, oral and
pharyngeal cavities.
The articulators are responsible for changing the properties of the system and form its output.
Combination of these cavities and articulators is called vocal tract. Its simplified acoustic

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model is represented in Figure 3.1.
Fig. 3.1 Vocal Tract Model
Speech production can be divided into three stages: first stage is the sound source production,
second stage is the articulation by vocal tract, and the third stage is sound radiation or
propagation from the lips and/or nostrils. A voiced sound is generated by vibratory motion of
the vocal cords powered by the airflow generated by expiration. The frequency of oscillation
of vocal cords is called the fundamental frequency. Another type of sounds -unvoiced sound
is produced by turbulent airflow passing through a narrow constriction in the vocal tract.
In a speaker recognition task, we are interested in the physical properties of human vocal
tract. In general it is assumed that vocal tract carries most of the speaker related information.
However, all parts of human vocal tract described above can serve as speaker dependent
characteristics. Starting from the size and power of lungs, length and flexibility of trachea and
ending by the size, shape and other physical characteristics of tongue, teeth and lips. Such
characteristics are called physical distinguishing factors. Another aspects of speech
production that could be useful indiscriminating between speakers are called learned factors,
which includes peaking rate, dialect, and prosodic effects.

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3.2.2 Vocal Model
In order to develop an automatic speaker identification system, we should construct
reasonable model of human speech production system. Having such a model, we can extract
its properties from the signal and, using them, we can decide whether or not two signals
belong to the same model and as a result to the same speaker.
Modeling process is usually divided into two parts: the excitation (or source) modeling and
the vocal tract modeling. This approach is based on the assumption of independence of the
source and the vocal tract models. Let us look first at the continuous-time vocal tract model
called multi tube lossless model, which is based on the fact that production of speech is
characterized by changing the vocal tract shape. Because the formalization of such a time-
varying vocal-tract shape model is quite complex, in practice it is simplified to the series of
concatenated lossless acoustic tubes with varying cross-sectional areas, as shown in Figure.
This model consists of a sequence of tubes with cross-sectional areas Ak and lengths Lk. In
practice the lengths of tubes assumed to be equal. If a large amount of short tubes is used,
then we can approach to the continuously varying cross-sectional area, but at the cost of more
complex model. Tract model serves as a transition to the more general discrete-time model,
also known as source-filter model, which is shown in Figure.
Fig. 3.2 Multi tube Lossless Model

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In this model, the voice source is either a periodic pulse stream or uncorrelated white noise, or
a combination of these. This assumption is based on the evidence from human anatomy that
all types of sounds, which can be produced by humans, are divided into three general
categories: voiced, unvoiced and combination of these two. Voiced signals can be modeled as
a basic or fundamental frequency signal filtered by the vocal tract and unvoiced as a white
noise also filtered by the vocal tract. Here E(z) represents the excitation function, H(z)
represents the transfer function, ands(n) is the output of the whole speech production system
[8]. Finally, we can think about vocal tract as a digital filter, which affects source signal and
about produced sound output as a filter output. Then based on the digital filter theory we can
extract the parameters of the system from its output
Fig.3.3 Source Filter Model
3.3 Speaker Identification
The human speech conveys different types of information. The primary type is the meaning or
words, which speaker tries to pass to the listener. But the other types that are also included in
the speech are information about language being spoken, speaker emotions, gender and
identity of the speaker. The goal of automatic speaker recognition is to extract, characterize
and recognize the information about speaker identity. Speaker recognition is usually divided
into two different branches, speaker verification and speaker identification. Speaker
verification task is to verify the claimed identity of person from his voice. This process
involves only binary decision about claimed identity. In speaker identification there is no
identity claim and the system decides who the speaking person is Speaker identification can

19
be further divided into two branches. Open-set speaker identification decides to whom of the
registered speakers‟ unknown speech sample belongs or makes a conclusion that the speech
sample is unknown. In this work, we deal with the closed-set speaker identification, which is
a decision making process of whom of the registered speakers is most likely the author of the
unknown speech sample. Depending on the algorithm used for the identification, the task can
also be divided into text-dependent and text-independent identification. The difference is that
in the first case the system knows the text spoken by the person while in the second case the
system must be able to recognize the speaker from any text.
The process of speaker identification is divided into two main phases.
1) Speaker enrollment
2) Speaker Identification
During the first phase, speaker enrollment, speech samples are collected from
The speakers and they are used to train their models. The collection of enrolled models is also
called a speaker database. Then in the enrollment phase, these features are modeled and stored
in the speaker database. This process is represented in following figure.
Fig. 3.4 Enrollment Phase
The next phase of speech recognition is identification phase which is shown below in the
figure. In the second phase, identification phase, a test sample from an unknown speaker is
compared against the speaker database. Both phases include the same first step, Feature
extraction, which is used to extract speaker dependent characteristics from speech. The main
purpose of this step is to reduce the amount of test data while retaining speaker discriminative
information.

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Fig. 3.5 Identification Phase
However, these two phases are closely related. For instance, identification algorithm usually
depends on the modeling algorithm used in the enrollment phase. This thesis mostly
concentrates on the algorithms in the identification phase and their optimization.

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CHAPTER 4
SPEECH FEATURE EXTRACTION
4.1 Introduction
The acoustic speech signal contains different kind of information about speaker. This includes
“high-level” properties such as dialect, context, speaking style, emotional state of speaker and
many others. A great amount of work has been already done in trying to develop
identification algorithms based on the methods used by humans to identify speaker. But these
efforts are mostly impractical because of their complexity and difficulty in measuring the
speaker discriminative properties used by humans. More useful approach is based on the
“low-level” properties of the speech signal such as pitch (fundamental frequency of the vocal
cord vibrations), intensity, formant frequencies and their bandwidths, spectral correlations,
short-time spectrum and others. From the automatic speaker recognition task point of view, it
is useful to think about speech signal as a sequence of features that characterize both the
speaker as well as the speech. It is an important step in recognition process to extract
sufficient information for good discrimination in a form and size which is amenable for
effective modeling. The amount of data, generated during the speech production, is quite large
while the essential characteristics of the speech process change relatively slowly and
therefore, they require less data.
According to these matters feature extraction is a process of reducing data while retaining
speaker discriminative information.
When dealing with speech signals there are some criteria that the extracted features should
meet. Some of them are listed below:
 discriminate between speakers while being tolerant of intra-speaker variability,
 easy to measure,
 stable over time,
 occur naturally and frequently in speech,
 change little from one speaking environment to another,
 Not be susceptible to mimicry.

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For MFCC feature extraction, we use the melcepst function from Voicebox. Because of its
nature, the speech signal is a slowly varying signal or quasi-stationary. It means that when
speech is examined over a sufficiently short period of time (20-30 milliseconds) it has quite
stable acoustic characteristics. It leads to the useful concept of describing human speech
signal, called “short-term analysis”, where only a portion of the signal is used to extract signal
features at one time. It works in the following way: predefined length window (usually 20-30
milliseconds) is moved along the signal with an overlapping (usually 30-50% of the window
length) between the adjacent frames. Overlapping is needed to avoid losing of information.
Parts of the signal formed in such a way are called frames. In order to prevent an abrupt
change at the end points of the frame, it is usually multiplied by a window function. The
operation of dividing signal into short intervals is called windowing and such segments are
called windowed frames (or sometime just frames). There are several window functions used
in speaker recognition area but the most popular is Hamming window function, which is
described by the following equation:
𝑊 𝑛 = 0.54 − 0.46cos⁡(
2𝑛𝜋
𝑁 − 1
)
Where N is the size of the window or frame. A set of features extracted from one frame is
called feature vector.
4.2 Short Term Analysis
Frame1, Frame2, Frame3, … Frame N the speech signal is slowly varying over time (quasi-
stationary), that is when the signal is examined over a short period of time (5-100msec), the
signal is fairly stationary. Therefore speech signals are often analyzed in short time segments,
which is referred to as short-time spectral analysis.

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Fig. 4.1 Short Term Analysis
4.3 Cepstrum
The speech signal s(n) can be represented as a “quickly varying” source signal e(n)
convolved with the “slowly varying” impulse response h(n) of the vocal tract represented as a
linear filter. We have access only to the output (speech signal) and it is often desirable to
eliminate one of the components. Separation of the source and the filter parameters from the
mixed output is in general difficult problem when these components are combined using not
linear operation, but there are various techniques appropriate for components combined
linearly. The cepstrum is representation of the signal where these two components are
resolved into two additive parts. It is computed by taking the inverse DFT of the logarithm of
the magnitude spectrum of the frame. This is represented in the following equation:

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𝐶𝑒𝑝𝑠𝑡𝑟𝑢𝑚 𝑓𝑟𝑎𝑚𝑒 = 𝐼𝐷𝐹𝑇 log 𝐷𝐹𝑇 𝑓𝑟𝑎𝑚𝑒
Some explanation of the algorithm is therefore needed. By moving to the frequency domain
we are changing from the convolution to the multiplication. Then by taking logarithm we are
moving from the multiplication to the addition. That is desired division into additive
components. Then we can apply linear operator inverse DFT, knowing that the transform will
operate individually on these two parts and knowing what Fourier transform will do with
quickly varying and slowly varying parts. Namely it will put them into different, hopefully
separate parts in new, also called quefrency axis.
4.3.1 Delta Cepstrum
The cepstral coefficients provide a good representation of the local spectral properties
of the framed speech. But, it is well known that a large amount of information resides
in the transitions from one segment of speech to another. An improved representation can
be obtained by extending the analysis to include information about the temporal cepstral
derivative. Delta Cepstrum is used to catch the changes between the different frames. Delta
Cepstrum defined as:
∆𝐶𝑠 𝑛; 𝑚 =
1
2
{𝐶𝑠 𝑛; 𝑚 + 1 − 𝐶𝑠(𝑛; 𝑚 − 1)}
𝑖 = 1, … , 𝑄
The results of the feature extraction are a series of vectors characteristic of the time-
varying spectral properties of the speech signal.

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Fig.4.2 Speech Magnitude Spectrum
We can see that the speech magnitude spectrum is combined from slow and quickly varying
parts. But there is still one problem: multiplication is not a linear operation. We can solve it
by taking logarithm from the multiplication as described earlier. Finally, let us look at the
result of the inverse DFT in Figure.
Fig.4.3Cepstrum
We can see that two components are clearly distinctive now.
4.4 Mel-Frequency Cepstrum Coefficients
In this project we are using Mel Frequency Cepstral Coefficient. Mel frequency Cepstral
Coefficients are coefficients that represent audio based on perception. This coefficient has a
great success in speaker recognition application. It is derived from the Fourier Transform of
the audio clip. In this technique the frequency bands are positioned logarithmically, whereas
in the Fourier Transform the frequency bands are not positioned logarithmically. As the
frequency bands are positioned logarithmically in MFCC, it approximates the human

26
system response more closely than any other system. These coefficients allow better
processing of data. In the Mel Frequency Cepstral Coefficients the calculation of the Mel
Cepstrum is same as the real Cepstrum except the Mel Cepstrum‟s frequency scale is warped
to keep up a correspondence to the Mel scale. The Mel scale was projected by Stevens,
Volkmann and Newman in 1937. The Mel scale is mainly based on the study of observing the
pitch or frequency Perceived by the human. The scale is divided into the units mel. In this test
the listener or test person started out hearing a frequency of 1000 Hz, and labeled it 1000 Mel
for reference. Then the listeners were asked to change the frequency till it reaches to the
frequency twice the reference frequency. Then this frequency labeled 2000 Mel. The same
procedure repeated for the half the frequency, then this frequency labeled as 500 Mel, and so
on. On this basis the normal frequency is mapped into the Mel frequency. The Mel scale is
normally a linear mapping below 1000 Hz and logarithmically spaced above 1000 Hz. Figure
below shows the example of normal frequency is mapped into the Mel frequency.
Mel-frequency cepstrum coefficients (MFCC) are well known features used to describe
speech signal. They are based on the known evidence that the information carried by low-
frequency components of the speech signal is phonetically more important for humans than
carried by high-frequency components. Technique of computing MFCC is based on the short-
term analysis, and thus from each frame a MFCC vector is computed. MFCC extraction is
similar to the cepstrum calculation except that one special step is inserted, namely the
frequency axis is warped according to the mel-scale. Summing up, the process of extracting
MFCC from continuous speech is illustrated in Figure.
A “mel” is a unit of special measure or scale of perceived pitch of a tone. It does not
correspond linearly to the normal frequency, indeed it is approximately linear below 1 kHz
and logarithmic above.

27
4.4.1Computing of melcepstrum coefficients
Fig. 4.4 Computing of mel-cepstrum coefficient
Figure above shows the calculation of the Mel Cepstrum Coefficients. Here we are using the
bank filter to warping the Mel frequency. Utilizing the bank filter is much more convenient to
do Mel frequency warping, with filters centered according to Mel frequency. According to the
Mel frequency the width of the triangular filters vary and so the log total energy in a critical
band around the center frequency is included. After warping are a number of coefficients.
Finally we are using the Inverse Discrete Fourier Transformer for the cepstral coefficients
calculation. In this step we are transforming the log of the quefrench domain coefficients to
the frequency domain. Where N is the length of the DFT we used in the cepstrum section.
To place more emphasize on the low frequencies one special step before inverse DFT in
calculation of cepstrum is inserted, namely mel-scaling. A “mel” is a unit of special measure
or scale of perceived pitch of a tone. It does not correspond linearly to the normal frequency,
indeed it is approximately linear below 1 kHz and logarithmic above. This approach is based
on the psychophysical studies of human perception of the frequency content of sounds. One

28
useful way to create mel-spectrum is to use a filter bank, one filter for each desired mel-
frequency component. Every filter in this bank has triangular band pass frequency response.
Such filters compute the average spectrum around each center frequency with increasing
bandwidths, as displayed in Figure.
Fig. 4.5 Triangular Filter used to compute mel-cepstrum
This filter bank is applied in frequency domain and therefore, it simply amounts to taking
these triangular filters on the spectrum. In practice the last step of taking inverse DFT is
replaced by taking discrete cosine transform(DCT) for computational efficiency.
The number of resulting mel-frequency cepstrum coefficients is practically chosen relatively
low, in the order of 12 to 20 coefficients. The zeroth coefficient is usually dropped out
because it represents the average log-energy of the frame and carries only a little speaker
specific information.
However, MFCC are not equally important in speaker identification and thus some
coefficients weighting might by applied to acquire more precise result. Different approach to
the computation of MFCC than described in this work is represented in that is simplified by
omitting filter bank analysis.

29
4.5 Framing and Windowing
The speech signal is slowly varying over time (quasi-stationary) that is when the signal is
examined over a short period of time (5-100msec), the signal is fairly stationary. Therefore
speech signals are often analyzed in short time segments, which are referred to as
short-time spectral analysis. This practically means that the signal is blocked in frames
of typically 20-30 msec. Adjacent frames typically overlap each other with 30-50%,
this is done in order not to lose any information due to the windowing.
Fig 4.6 Quasi Stationary
After the signal has been framed, each frame is multiplied with a window function w(n) with
length N, where N is the length of the frame. Typically the Hamming window is used:
𝑊 𝑛 = 0.54 − 0.46cos⁡(
2𝜋𝑛
𝑁 − 1
)
Where 0 ≤ 𝑛 ≤ 𝑁 − 1
The windowing is done to avoid problems due to truncation of the signal as windowing helps
in the smoothing of the signal.

30
CHAPTER 5
VECTOR QUANTIONZATION
A speaker recognition system must able to estimate probability distributions of the
computed feature vectors. Storing every single vector that generate from the training mode
is impossible, since these distributions are defined over a high-dimensional space. It is
often easier to start by quantizing each feature vector to one of a relatively small number of
template vectors, with a process called vector quantization. VQ is a process of taking a large
set of feature vectors and producing a smaller set of measure vectors that represents the
centroids of the distribution.
The technique of VQ consists of extracting a small number of representative feature vectors
as an efficient means of characterizing the speaker specific features. By means of VQ, storing
every single vector that we generate from the training is impossible.
Vector quantization (VQ) is a process of mapping vectors from a vector space to a finite
number of regions in that space. These regions are called clusters and represented by their
central vectors or centroids. A set of centroids, which represents the whole vector space, is
called a codebook. In speaker identification, VQ is applied on the set of feature vectors
extracted from the speech sample and as a result, the speaker codebook is generated. Such
codebook has a significantly smaller size than extracted vector set and referred as a speaker
model.
Actually, there is some disagreement in the literature about approach used in VQ. Some
authors consider it as a template matching approach because VQ ignores all temporal
variations and simply uses global averages (centroids). Other authors consider it as a
stochastic or probabilistic method, because VQ uses centroids to estimate the modes of a
probability distribution. Theoretically it is possible that every cluster, defined by its centroid,
models particular component of the speech. But practically, however, VQ creates
unrealistically clusters with rigid boundaries in a sense that every vector belongs to one and
only one cluster. Mathematically a VQ task is defined as follows: given a set of feature
vectors, find a partitioning of the feature vector space into the predefined number of regions,
which do not overlap with each other and added together form the whole feature vector space.
Every vector inside such region is represented by the corresponding centroid. The process of
VQ for two speakers is represented in Figure.

31
Fig. 5.1 Vector Quantization of Two Speakers
There are two important design issues in VQ: the method for generating the codebook and
codebook size. Known clustering algorithms for codebook generation are:
 Generalized Lloyd algorithm (GLA),
 Self-organizing maps (SOM),
 Pair wise nearest neighbor (PNN),
 Iterative splitting technique (SPLIT),
 Randomized local search (RLS).
 K means
According to, iterative splitting technique should be used when the running time is important
but RLS is simpler to implement and generates better codebooks in the case of speaker

32
identification task. Codebook size is a trade-off between running time and identification
accuracy. With large size, identification accuracy is high but at the cost of running time and
vice versa. Experimental result obtained in is that saturation point choice is 64 vectors in
codebook. The quantization distortion (quality of quantization) is usually computed as the
sum of squared distances between vector and its representative (centroid). The well-known
distance measures are Euclidean, city block distance.

33
CHAPTER 6
K MEANS
The K-means algorithm partitions the T feature vectors into M centroids. The algorithm first
chooses M cluster-centroids among the T feature vectors. Then each feature vector is assigned
to the nearest centroid, and the new centroids are calculated. This procedure is continued until
a stopping criterion is met, that is the mean square error between the feature vectors and the
cluster-centroids is below a certain threshold or there is no more change in the cluster-center
assignment.
In data mining, k-means clustering is a method of cluster analysis which aims
to partition n observations into k clusters in which each observation belongs to the cluster
with the nearest mean. This results into a partitioning of the data space into cells. The problem
is computationally difficult (NP-hard), however there are efficient heuristic algorithms that
are commonly employed and converge fast to a local optimum. These are usually similar to
the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative
refinement approach employed by both algorithms. Additionally, they both use cluster centers
to model the data, however k-means clustering tends to find clusters of comparable spatial
extent, while the expectation-maximization mechanism allows clusters to have different
shapes.
The most common algorithm uses an iterative refinement technique. Due to its ubiquity it is
often called the k-means algorithm; it is also referred to as Lloyd's algorithm, particularly in
the computer science community.
Given an initial set of k means m1
(1)
,…,mk
(1)
(see below), the algorithm proceeds by
alternating between two steps:

34
Where each 𝑥 𝑝 goes into exactly one 𝑆𝑖
(𝑡)
, even if it could go in two of them. Update step:
Calculate the new means to be the centroid of the observations in the cluster.
The algorithm is deemed to have converged when the assignments no longer change.
Commonly used initialization methods are Forgy and Random Partition. The Forgy method
randomly chooses k observations from the data set and uses these as the initial means. The
Random Partition method first randomly assigns a cluster to each observation and then
proceeds to the Update step, thus computing the initial means to be the centroid of the
cluster's randomly assigned points. The Forgy method tends to spread the initial means out,
while Random Partition places all of them close to the center of the data set. According to
Hamerly et al., the Random Partition method is generally preferable for algorithms such as the
k-harmonic means and fuzzy k-means. For expectation maximization and standard k-means
algorithms, the Forgy method of initialization is preferable.
Given a set of observations (x1, x2, …, xn), where each observation is a d-dimensional real
vector, k-means clustering aims to partition the n observations into k sets
(k ≤ n) S = {S1, S2, …, Sk} so as to minimize the within-cluster sum of squares (WCSS):
Where μi is the mean of points in Si.
The two key features of k-means which make it efficient are often regarded as its biggest
drawbacks:
 Euclidean distance is used as a metric and variance is used as a measure of cluster scatter.
 The number of clusters k is an input parameter: an inappropriate choice of k may yield
poor results. That is why, when performing k-means, it is important to run diagnostic
checks for determining the number of clusters in the data set.
A key limitation of k-means is its cluster model. The concept is based on spherical clusters
that are separable in a way so that the mean value converges towards the cluster center. The
clusters are expected to be of similar size, so that the assignment to the nearest cluster center

35
is the correct assignment. When for example applying k-means with a value of onto
the well-known Iris flower data set, the result often fails to separate the three Iris species
contained in the data set. With , the two visible clusters (one containing two species)
will be discovered, whereas with one of the two clusters will be split into two even
parts. In fact, is more appropriate for this data set, despite the data set containing
3 classes. As with any other clustering algorithm, the k-means result relies on the data set to
satisfy the assumptions made by the clustering algorithms. It works very well on some data
sets, while failing miserably on others.
The result of k-means can also be seen as the Voronoi cells of the cluster means. Since data is
split halfway between cluster means, this can lead to suboptimal splits as can be seen in the
"mouse" example. The Gaussian models used by the Expectation-maximization
algorithm (which can be seen as a generalization of k-means) are more flexible here by having
both variances and covariance. The EM result is thus able to accommodate clusters of variable
size much better than k-means as well as correlated cluster.
k-means clustering in particular when using heuristics such as Lloyd's algorithm is rather easy
to implement and apply even on large data sets. As such, it has been successfully used in
various topics, ranging from market segmentation, computer vision, geostatistics
and astronomy to agriculture. It often is used as a preprocessing step for other algorithms, for
example to find a starting configuration.
k-means clustering, and its associated expectation-maximization algorithm, is a special case of
a Gaussian mixture model, specifically, the limit of taking all covariance as diagonal, equal,
and small. It is often easy to generalize a k-means problem into a Gaussian mixture model.
6.1Mean shift clustering
Basic mean shift clustering algorithms maintain a set of data points the same size as the input
data set. Initially, this set is copied from the input set. Then this set is iteratively replaced by
the mean of those points in the set that are within a given distance of that point. By
contrast, k-means restricts this updated set to k points usually much less than the number of
points in the input data set, and replaces each point in this set by the mean of all points in
the input set that are closer to that point than any other (e.g. within the Voronoi partition of
each updating point). A mean shift algorithm that is similar then to k-means, called likelihood
mean shift, replaces the set of points undergoing replacement by the mean of all points in the

36
input set that are within a given distance of the changing set. One of the advantages of mean
shift over k-means is that there is no need to choose the number of clusters, because mean
shift is likely to find only a few clusters if indeed only a small number exist. However, mean
shift can be much slower than k-means. Mean shift has soft variants much as k-means does.
6.2 Bilateral filtering
k-means implicitly assumes that the ordering of the input data set does not matter.
The bilateral filter is similar to K-means and mean shift in that it maintains a set of data points
that are iteratively replaced by means. However, the bilateral filter restricts the calculation of
the (kernel weighted) mean to include only points that are close in the ordering of the input
data. This makes it applicable to problems such as image denoising, where the spatial
arrangement of pixels in an image is of critical importance.
6.3 Speaker Matching
During the matching a matching score is computed between extracted feature vectors and
every speaker codebook enrolled in the system. Commonly it is done as a partitioning
extracted feature vectors, using centroids from speaker codebook, and calculating matching
score as a quantization distortion.
Another choice for matching score is mean squared error (MSE), which is computed as the
sum of the squared distances between the vector and nearest centroid divided by number of
vectors extracted from the speech sample.
The quantization distortion (quality of quantization) is usually computed as the sum of
squared distances between vector and its representative (centroid). The well-known distance
measures are Euclidean, city block distance, weighted Euclidean and Mahalanobis. They are
represented in the following equations:

37
Where x and y are multi-dimensional feature vectors and D is a weighting matrix. When D is
a covariance matrix weighted Euclidean distance also called Mahalanobis distance. Weighted
Euclidean distance where D is a diagonal matrix and consists of diagonal elements of
covariance matrix is more appropriate, in a sense that it provides more accurate identification
result. The reason for such result is that because of their nature not all components in feature
vectors are equally important and weighted distance might give more precise result.

38
CHAPTER 7
EUCLIDEAN DISTANCE
In mathematics, the Euclidean distance or Euclidean metric is the "ordinary" distance between
two points that one would measure with a ruler, and is given by the Pythagorean formula. By
using this formula as distance, Euclidean space (or even any inner product space) becomes
a metric space. The associated norm is called the Euclidean norm. Older literature refers to the
metric as Pythagorean metric.
The Euclidean distance between points p and q is the length of the line segment connecting
them ( ).
In Cartesian coordinates, if p = (p1, p2,..., pn) and q = (q1, q2,..., qn) are two points
in Euclidean n-space, then the distance from p to q, or from q to p is given by:
The position of a point in a Euclidean n-space is a Euclidean vector. So, p and q are Euclidean
vectors, starting from the origin of the space, and their tips indicate two points. The Euclidean
norm, or Euclidean length, or magnitude of a vector measures the length of the vector:
Where the last equation involves the dot product.
A vector can be described as a directed line segment from the origin of the Euclidean space
(vector tail), to a point in that space (vector tip). If we consider that its length is actually the
distance from its tail to its tip, it becomes clear that the Euclidean norm of a vector is just a
special case of Euclidean distance: the Euclidean distance between its tail and its tip.

39
The distance between points p and q may have a direction (e.g. from p to q), so it may be
represented by another vector, given by:
In a three-dimensional space (n=3), this is an arrow from p to q, which can be also regarded as
the position of q relative to p. It may be also called a displacement vector if p and q represent
two positions of the same point at two successive instants of time.
The Euclidean distance between p and q is just the Euclidean length of this distance (or
displacement) vector:
Which is equivalent to equation 1, and also to:
7.1One dimension
In one dimension, the distance between two points on the real line is the absolute value of
their numerical difference. Thus if x and y are two points on the real line, then the distance
between them is given by:
In one dimension, there is a single homogeneous, translation-invariant metric (in other words,
a distance that is induced by a norm), up to a scale factor of length, which is the Euclidean
distance. In higher dimensions there are other possible norms.

40
7.2 Two dimensions
In the Euclidean plane, if p = (p1, p2) and q = (q1, q2) then the distance is given by
This is equivalent to the Pythagorean theorem.
Alternatively, it follows from (2) that if the polar coordinates of the point p are (r1, θ1) and
those of q are (r2, θ2), then the distance between the points is
7.3 Three dimensions
In three-dimensional Euclidean space, the distance is
7.4 N dimensions
In general, for an n-dimensional space, the distance is
7.5 Squared Euclidean Distance
The standard Euclidean distance can be squared in order to place progressively greater weight
on objects that are further apart. In this case, the equation becomes

41
Squared Euclidean Distance is not a metric as it does not satisfy the triangle inequality,
however it is frequently used in optimization problems in which distances only have to be
compared.
It is also referred to as quadrance within the field of rational trigonometry.
7.6 Decision making
The next step after computing of matching scores for every speaker model enrolled in the
system is the process of assigning the exact classification mark for the input speech. This
process depends on the selected matching and modeling algorithms. In template matching,
decision is based on the computed distances, whereas in stochastic matching it is based on the
computed probabilities.
Fig. 7.1 Decision Process
In the recognition phase an unknown speaker, represented by a sequence of feature vectors is
compared with the codebooks in the database. For each codebook a distortion measure is
computed, and the speaker with the lowest distortion is chosen as the most likely speaker.

42
CHAPTER 8
RESULT AND CONCLUSION
8.1 Result
We show the results of our experiments. Every chart is preceded by the short explanation
what we measured and why and followed by the short discussion about results.
8.2 Pruning Basis
First, we start from the basis for speaker pruning. We ran a few tests to see how the matching
function behaves during the identification and when it stabilizes. Chart in Figure 7.1
represents the variation of matching score depending on the available test vectors for 20
different speakers. One vector refers to the one analysis frame.
Fig: 8.1 Distance stabilization
In this figure, the bold line represents the owner of the test sample. It can be seen that at the
beginning the matching score for correct speaker is somewhere among the other scores. But
after large enough amount of new vectors extracted from the test speech, it becomes close
with only few score sand at the end it becomes the smallest score. Actually, this is the

43
underlying reason for speaker pruning: when we have more data we can drop some of the
models from identification.
8.2.1 Static pruning
In the next experiment, we consider the trade-off between identification error rate and average
time spent on the identification for static pruning. By varying the pruning interval or number
of pruned speakers we expect different error rates and different identification times. From
several runs with different parameter combination we can plot the error rate as a function of
average identification time. To obtain such dependency, we fixed three values for pruning
interval and were varying the number of pruned speakers. The results are shown in Figure 7.3.
Fig: 8.2Evaluation of static variant using different pruning intervals
From this figure we can see that all curves follow almost the same shape. It is because in
order to have fast identification we have to choose either small pruning interval or large
number of pruned speakers. On the other hand, in order to have low error rate we have to
choose large interval or small number of pruned speakers. The main conclusion from this
figure is that these two parameters compensate each other.

44
8.2.2 Adaptive pruning
The idea of adaptive pruning is based on the assumption that distribution of matching score
follows more or less the Gaussian curve. In Figure 7.4 we can see the distributions of
matching scores for two typical identifications.
Fig: 8.3 Examples of the matching score distribution
From this figure we can see that distribution is not exactly follows the Gaussian curve but its
shape is almost the same. In the next experiment, we consider the trade-off between
identification error rate and average time spenton the identification for adaptive pruning. By
fixing the parameter ŋ and varying the pruning interval we obtained desired dependency.
8.3 Conclusion
The goal of this project was to create a speaker recognition system, and apply it to a speech of
an unknown speaker. By investigating the extracted features of the unknown speech and
then compare them to the stored extracted features for each different speaker in order to
identify the unknown speaker.

45
The feature extraction is done by using MFCC (Mel Frequency Cepstral Coefficients). The
function „melcepst‟ is used to calculate the melcepstrum of a signal. The speaker was modeled
using Vector Quantization (VQ). A VQ codebook is generated by clustering the training
feature vectors of each speaker and then stored in the speaker database. In this method, the
K means algorithm is used to do the clustering. In the recognition stage, a distortion measure
which based on the minimizing the Euclidean distance was used when matching an unknown
speaker with the speaker database.
During this project, we have found out that the VQ based clustering approach provides
us with the faster speaker identification process.

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