speech recognition

B.Tech Project Interim Report I

CHAPTER 1

INTRODUCTION

The current practice of taking attendance in a lecture class is simply calling

the roll numbers by each student and marking it by the teacher. This is a time

consuming process and the accuracy of this process is low. It's found that the

students those who are not present in the classroom, get attendance by doing some

malpractices. There is a great chance for doing malpractices by the student. And

it's complex to enter and calculate student's overall attendance, and sometimes

may have chance not to get attendance even though he/she is present in the class.

It seems to be very difficult to avoid these limitations, even if effectively the

process had followed.

In determining the internals of every student’s, the attendance has a 10%

role. The malpractices in taking attendance, thus will affect the internals of the

students. The idea about this project had developed in our mind, while thinking

about a scientific way to register the attendance. Thus the defects in the ordinary

attendance taking practice can be reduced by a large extent. The various biometric

characteristics that are generally used are the face, iris, fingerprints palm prints

hand geometry and the behavioral characteristics include signature voice pattern.

Biometrics is the science or technology which analyses and measures the

biological data. In computer science it refers to science or technology that

measure and analyzes physical or behavioral characteristics of a person, for

authentication.

Voice recognition or speaker recognition systems extract features from the

speech using MATLAB and model them to use for recognition these systems use

the acoustic features present in the speech which are unique for each individual.

These acoustic pattern depend on the physical characteristics of individual (e.g.:

the size of mouth and throat) as well as behavioral characteristics like speaking

styles and voice pitch. Everyone has a distinct voice, different from all others;

almost like a fingerprint, one’s voice is unique and can act as an identifier. The

Department of ECE 1 Thejus Engineering College


human voice is composed of a multitude of different components, making each

voice different; namely Pitch and Tone.

Speech is one of the natural forms of communication recent developments

have made it possible to use this in the security system. In speaker identification

task use a speech sample to select the identity of person that produce the speech

from a population of speakers. In speaker verification the task is to use a speech

sample to test whether a person who claims to have produced the speech has in

fact done so. This technique makes it possible to use the speaker voice to verify

their identity and control access to services such as voice dialing banking by

telephone ,attendance marking ,telephone shopping ,data base access

services ,information services ,voice mail ,security control for confidential

information areas and remote access to computers.

Speaker recognition methods can be divided into text independent and text

dependent methods. In a text independent system, speaker models capture

characteristics of somebody’s speech which show up irrespective what one is

saying. In a text dependent system, on the other hand, the recognition of the

speaker’s identity is based on his or her speaking one or more specific phases, like

password, card numbers, PIN codes. Every technology of speaker

recognition ,identification and verification whether text independent and text

dependent ,each has its own advantages and disadvantages and may require

different treatment and techniques the choice of which technology to use is

application specific. At the highest level all the speaker recognition system

contain two main modules feature extraction and feature matching.

Overview of the project

VoDAR (Voice Detected Attendance Register), is a system that register

the attendance of each student, at high accuracy. Matlab is the tool for our project.

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. Feature extraction and feature matching are the main process

in this project. Feature extraction done using MFCC.



The main purpose of the MFCC processor is to mimic the behavior of the

human ears. In addition MFCCs are shown to be less susceptible to mentioned

variations. Speaker identification is done by vector quantization, which consists of

comparing a speech signal from an unknown speaker database to a database of

known speakers.

A sequence of feature vectors {x1,…, xT}, 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. VQ based clustering approach is best

as it provides us with the faster speaker identification process.

Report organization

In chapter two a literature review is conducted. This chapter presents the

detailed data collections necessary for our project. Information from international

journals are included here. This chapter gives insight to our project by clarifying

various steps of voice recognition such as feature extraction and feature matching.

In chapter three a comparative study with the existing system has been

done. Here comparative study with other biometric attendance systems are made

which include face recognition attendance system, Finger print attendance system,

Iris recognition attendance system, Palm print recognition attendance system,

Hand geometry recognition attendance system, signature recognition attendance

system etc. This chapter also deals with the advantages of VoDAR..

Detailed description of working of VODAR has been done in chapter four.

Human voice generation and types of speech recognition are also main contents of

this chapter. The feature extraction by MFCC (Mel filter Cepstral Coefficient) [3]

and speaker identification by vector quantization [4] are also included here.

Finally the reason for choosing Matlab is also included here. In chapter five we

concluded that, from various biometric systems, voice recognition is best suited

for our application. In the end the list of references are included. In appendix,

codes for recording speech signal and pre-emphasis of speech signal that were

implemented in this project are provided.



CHAPTER 2

OVERVIEW OF VOICE RECOGNITION

Speech is one of the most dominating and natural means of

communication for expressing our ideas, emotions. Voice recognition involves the

process of extracting usable information from speech signal and using which the

person identification is performed.

Speaker recognition is the computing task of validating a user's claimed

identity using characteristics extracted from their voices. Voice recognition uses

learned aspects of a speaker’s voice to determine who is talking. Such a system

cannot recognize speech from random speakers very accurately, but it can reach

high accuracy for individual voices it has been trained with, which gives us

various applications in day today life. This study introduced various methods of

speaker identification involving LPC, MFCC [3] feature extraction. Linear

prediction is a mathematical operation which provides an estimation of the current

sample of a discrete signal as a linear combination of several previous samples.

The prediction error which is the difference between the predicted and actual

value is called the residual. Using this idea feature extraction is implemented in

LPC feature extraction method, where as in MFCC the log of signal energy is

calculated.

Mrs. Arundhathi proposes design of an automatic speaker recognition

system [4] utilizing the concept of MFCC. MFCC [4] are derived from a type of

cepstral representation of the audio clip. The difference between the cepstrum and

the Mel Frequency Cepstrum (MFC) [4] is that the frequency bands are equally

spaced on the Mel scale, which approximates the human auditory system's

response more closely than the linearly-spaced frequency bands used in the

normal cepstrum. The cepstrum is a common transform used to gain information

from a person’s speech signal. It can be used to separate the excitation signal

(which contains the words and the pitch) and the transfer function (which contains

the voice quality). It is the result of taking Fourier transform of decibel spectrum

as if it were a signal. We use cepstral analysis in speaker identification because

the speech signal is of the particular form above, and the "cepstral transform" of it



makes analysis incredibly simple. Mathematically, cepstrum of signal =

FT[log{FT(the windowed signal)}] MFCC[4] are commonly calculated by first

taking the Fourier transform of a windowed excerpt of a signal and mapping the

powers of the spectrum obtained above onto the Mel scale, using triangular

overlapping windows. Next the logs of the powers at each of the Mel frequencies

are taken; Discrete Cosine Transform is applied to it (as if it were a signal). The

MFCC’s are the amplitudes of the resulting spectrum. The speech input is

typically recorded at a sampling rate above 10000 Hz. This sampling frequency

was chosen to minimize the effects of aliasing in the analog-to-digital conversion.

These sampled signals can capture all frequencies up to 5 kHz, which cover most

energy of sounds that are generated by humans. The main purpose of the MFCC

[5] processor is to mimic the behavior of the human ears. In addition MFCCs [5]

are shown to be less susceptible to mentioned variations. The feature extraction

using MFCC [5] is utilized for speaker identification.

Mr. Manoj kaur describes the vector quantization based speaker

identification [6] such that, a speaker recognition system must be 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 [6] 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. By using these training data features are clustered to

form a codebook for each speaker. In the recognition stage, the data from the

tested speaker is compared to the codebook of each speaker and measure the

difference. These differences are then use to make the recognition decision.

Survey of biometric recognition systems and their applications (journal of

theoretical and applied information technology) [7] is a journal which describes

about various biometric systems available and their peculiarities. The human

physical characteristics like fingerprints, face, voice and iris are known as

biometrics. This study helped us to understand various biometric systems

available. There by choosing voice recognition system as best suited for our



application that is, as an attendance register. Facial recognition have

disadvantages like complex system, time consuming compared to voice. As well

recognition is affected by changes in lighting, the age and if the person wears

glasses. It requires camera equipment for user identification which is costly. Iris

recognition has disadvantages such as large storage requirement and expensive.

Finger tip recognition can make mistakes due to the dryness or dirt in the finger’s

skin, as well as with the age . It demands a large memory and Compression is

required (a factor of 10 approximately). Hence out of all most suitable for our

application is voice which is cheap and speaker verification time is only 5

seconds.

MFCC and its applications in speaker recognition [8] describes that

Speech processing is emerged as one of the important application area of digital

signal processing. Various fields for research in speech processing are speech

recognition, speaker recognition, speech synthesis, speech coding etc. The

objective of automatic speaker recognition is to extract, characterize and

recognize the information about speaker identity. Feature extraction is the first

step for speaker recognition. Many algorithms are developed by the researchers

for feature extraction. In this work, the Mel Frequency Cepstrum Coefficient

(MFCC) feature has been used for designing a text dependent speaker

identification system. Some modifications to the existing technique of MFCC for

feature extraction are also suggested to improve the speaker recognition

efficiency. Another important point emerged from this paper is that, as no. of filter

in filter bank increases the efficiency also increases. Also these reveled that

compared to rectangular window hanning window has more efficiency.

Vector quantization using speaker identification [9] describes about the

methodology followed in this paper for speaker identification, which consists of


speakers. The methodology followed in this paper for Speaker identification is

using Feature Extraction process and then Vector Quantization of extracted

features is done using k-means algorithm. The K-means algorithm is widely used

in speech processing as a dynamic clustering approach. “K” is pre-selected and

simply refers to the number of desired clusters. In the recognition phase an

unknown speaker, represented by a sequence of feature vectors {x1,…, xT}, 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. VQ

based clustering approach is best as it provides us with the faster speaker

identification process.



CHAPTER 3

CURRENT SCENARIO

3.1 DRAWBACKS OF EXISTING SYSTEMS

The study of existing systems such as face recognition system, finger print

recognition system, iris recognition system, palm recognition system, hand

recognition system, hand geometry, signature recognition ,voice recognition

system were analyzed for our particular application of attendance marking.

3.1.1 Face recognition attendance system:

Humans have a remarkable ability to recognize fellow beings based on

facial appearance. So, face is a natural human trait for automated biometric

recognition. Face recognition systems typically utilize the spatial relationship

among the locations of facial features such as eyes, nose, lips, chin, and the global

appearance of a face. The forensic and civilian applications of face recognition

technologies pose a number of technical challenges for static photograph matching

(e.g., for ensuring that the same person is not requesting multiple passports). The

problems associated with illumination, gesture, facial makeup, occlusion, and

pose variations adversely affect the face recognition performance. While face

recognition is non-intrusive, robust face recognition in non-ideal situations

continues to pose challenges.

3.1.2 Fingerprint recognition attendance system:

Fingerprint-based recognition has been the longest serving, and popular

method for person identification. Fingerprints consist of a regular texture pattern

composed of ridges and valleys. These ridges are characterized by several

landmark points, known as minutiae, which are mostly in the form of ridge

endings and ridge bifurcations. The spatial distribution of these minutiae points is

claimed to be unique to each finger; it is the collection of minutiae points in a

fingerprint that is primarily employed for matching two fingerprints. In addition to

minutiae points, there are sweat pores and other details (referred to as extended)



which can be acquired in high resolution fingerprint images. However, there are

some disadvantages in this system. If the surface of the finger gets damaged

and/or has one or more marks on it, identification becomes increasingly hard.

Furthermore, the system requires the user’s finger surface to have a point of

minutiae or pattern in order to have matching images. This will be a limitation

factor for the security of the algorithm

3.1.3 Iris recognition attendance system:

The iris is the colored annular ring that surrounds the pupil. Iris images

acquired under infrared illumination consist of complex texture pattern with

numerous individual attributes, e.g. stripes, pits, and furrows, which allow for

highly reliable personal identification. The iris is a protected internal organ whose

texture is stable and distinctive, even among identical twins (similar to

fingerprints), and extremely difficult to surgically spoof. However, relatively high

sensor cost, along with relatively large failure to enroll (FTE) rate reported in

some studies, and lack of legacy iris databases may limit its usage in some large-

scale government application.

3.1.4 Palm print recognition attendance system:

The image of a human palm consists of palm are friction ridges and

flexion creases (lines formed due to stress). Similar to fingerprints, latent palm

print systems utilize minutiae and creases for matching. Based on the success of

fingerprints in civilian applications, some attempts have been made to utilize low

resolution palm print images for access control applications .These systems utilize

texture features which are quite similar to those employed for iris recognition.

Palm print recognition systems have not yet been deployed for civilian

applications (e.g., access control), mainly due to their large physical size and the

fact that fingerprint identification based on compact and embedded sensors works

quite well for such applications.



3.1.5 Hand Geometry recognition attendance system:

It is claimed that individuals can be discriminated based on the shape of

their hands. Person identification using hand geometry utilizes low resolution

hand images to extract a number of geometrical features such as finger length,

width, thickness, perimeter, and finger area. The discriminatory power of these

features is quite limited, and therefore hand geometry systems are employed only

for verification applications in low security access control and for attendance

marking application, geometry systems require large physical size, so they cannot

be easily embedded in existing security systems.

3.1.6 Signature recognition attendance system:

Signature is a behavioral biometric model that is used in daily business

transactions (e.g., credit card purchase). However, attempts to develop highly

accurate signature recognition systems have not been successful. This is primarily

due to the large variations in a person’s signature over time. Attempts have been

made to improve the signature recognition performance by capturing dynamic or

online signatures that require pressure-sensitive pen-pad. Dynamic signatures help

in acquiring the shape, speed, acceleration, pen pressure, order and speed of

strokes, during the actual act of signing. This additional information seems to

improve the verification performance (over static signatures) as well as

circumvent signature forgeries. Still, very few automatic signature verification

systems have been deployed, because of increased interference such as dirtiness,

injury, roughness.



3.2 COMPARITVE STUDY OF EXISTING SYSTEMS

Features Eye-Iris Eye-

Retina

Finger

print

Signature Voice

Reliability Very

High

Very High High High High

Easiness of

the use

Average Low High High High

Social

Acceptance

Low Low Medium High High

Interference Glasses Irritation Dirtiness

Injury

Roughness

Changeable

Easy

signature

Noise

Cost High High Medium Low Low

Device

Required

Camera Camera Scanner Optic pen

Touch

panel

Microphone

Table 1: Comparative study of existing systems

From the above table it can be seen that voice recognition is

comparatively less costly and sufficiently accurate. The device required for voice

recognition is easily available and low cost compared to other systems. Hence it’s

preferred over other systems for attendance marking.

3.3 ADVANTAGES OF VoDAR OVER EXISTING SYSTEMS

The advantage of VoDAR is listed below. Mainly there are five

advantages. They are ability to use technology remotely, low cost of using it, high

reliability rate, ease of use and ease of implementation and minimally invasive.



3.3.1 Ability to Use Technology remotely

One of the main advantages of voice verification technology is the ability

to use it remotely. Many other types of biometrics cannot be used remotely, such

as fingerprints, retina biometrics or iris biometrics one of the advantages of speech

recognition technology is that, it’s easy to use over the phone or other speaking

devices, increasing its usefulness to many companies. The ability to use it

remotely makes it stand out among many other types of biometric technology

available today.

3.3.2 Low Cost of Using It

The low cost of this technology is another advantage of voice recognition.

The price of acquiring a voice recognition system is usually quite reasonable,

especially when compared to the price of other biometric systems. These systems

are relatively low cost to implement and maintain and the equipment needed is

low priced as well. Very little equipment is needed for these systems, making it a

cost effective option for businesses. In many cases, all that is required for these

systems to function is the right biometric software if the technology is being used

remotely over the phone. The phone acts as the speaking device, so there is no

investment in this device. For systems being used for authentication and

verification on sites, businesses only have to worry about purchasing a device that

users can speak into along with the speech recognition software.

3.3.3 High Reliability Rate

Another advantage of voice recognition is this technology’s high reliability

rate. 10-20 years ago, the reliability rate of speech recognition technology was

actually quite low. There were many problems that produced reliability problems,

such as the inability to deal with background noise or the inability to recognize

voices when an individual had a slight cold. However, these problems have been

dealt with successfully today, giving this biometric technology a very high

reliability rate. Vocal prints now can easily be used to identify an individual, even

if their speech sounds a bit different due to a cold. One of the advantages of these


http://www.biometric-security-devices.com/biometric-technologies.html

http://www.biometric-security-devices.com/purchase-biometric-devices.html


types of systems is that they are designed to ignore background noise and focus on

the voice, which also has given the reliability rate a huge boost.

3.3.4 Ease of Use and Implementation

Many companies really appreciate the ease of use and implementation that

comes with voice recognition biometrics. Some biometric technologies can be

difficult to implement into a company and difficult to begin using. Since these

systems require minimal equipments, so they can usually be implemented without

the addition of new equipment and systems. Since they are so easy to use,

companies can often reduce their personnel and make use of them elsewhere in

the company to improve performance and customer satisfaction.

3.3.5 Minimally Invasive

One of the major advantages of this system is that it is minimally invasive,

which is one of the big advantages of voice recognition. This is very important to

individuals that use these security devices. Many consumers today do not like

many forms of biometric technology, since other forms seem so invasive. The

advantages of speech technology are that it only requires individuals to speak and

offer a vocal sample, which is minimally invasive. Since this technology has a

high approval rate among consumers, it can help businesses keep their customers

happy with the service they are providing.


http://www.biometric-security-devices.com/voice-recognition-biometrics.html


CHAPTER 4

SYSTEM DESIGN FOR VoDAR

4.1 BLOCK DIAGRAM DESCRIPTION

The main aim of this project is speaker identification, which consists of


speaker database. The system can recognize the speaker, which has been trained

with a number of speakers.

Fig 4.1: Block diagram of VoDAR

Above 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 used as the key to confirm the

identities of a speaker which is known as speaker verification .The system consists


Conversion to vector form

Conversion to corpus sentences

Collection of corpus sentences

Comparison

Mica input

Known speech

Unknown speech

Decision


of a microphone connected to a computer system. The voice inputs of each student

are recorded via mice, and each input is analyzed by the system by the MATLAB

software. MATLAB is the software tool of our project. First we have to store

some reference voice signal wave form, with the help of a microphone and the

computer. These stored speech signals are called corpus sentences .By the help

of MATLAB software, these waveforms get analyzed and we convert each speech

signal into vector form. Now the input voice signals from the students are also

converted into the vector form. After comparing this vector sentence with the

corpus sentences, the most similar corpus sentence will be determined. Thus

speaker identification is carried out and corresponding attendance will be marked.

4.2 HUMAN VOICE GENERATION

Fig4.2: Voice generation

Consider the anatomy and physiology of the voice by following the voice

from the lungs to the lips. The breath stream, referred to as the "generator" of the



voice, originates in the lungs. This generator provides a controlled flow of air

which powers the vocal folds by setting them into motion.

The human larynx has three vital functions. They are...

1. Airway protection (prevention of aspiration)

2. Respiration (breathing)

3. Phonation (talking)

When human speak, the vocal folds approximate and vibrate to produce voice.

When personals breathe the vocal folds open or abduct and allow air to flow from

the lungs through the mouth and nose and vice versa. When human eat, we

reflexively stop breathing and the vocal folds approximate to protect the airway

and keep food and drink out of the lungs. The speech signal is given by

Fig4.3: Speech signal (amplitude v/s time)

The vocal folds do not operate like strings on a violin but actually are more

comparable to vibrating lips "buzzing". The three-dimensional cavity, or


amplitude


"resonator", that provides sound modification. The articulators (the parts of

the vocal tract above the larynx consisting of tongue, palate, cheek, lips,

etc.) articulate and filter the sound emanating from the larynx and to some degree

can interact with the laryngeal airflow to strengthen it or weaken it as a sound

source. Adult men and women have different sizes of vocal fold; reflecting the

male-female differences in larynx size. Adult male voices are usually lower-

pitched and have larger folds. The male vocal folds (which would be measured

vertically in the opposite diagram), are between 17 mm and 25 mm in length.] The

female vocal folds are between 12.5 mm and 17.5 mm in length. The difference in

vocal folds size between men and women means that, they have differently

pitched voices. Additionally, genetics also causes variances amongst the same sex,

with men's and women's singing voices being categorized into types.

4.3 SPEECH RECOGNITION

The structure of a typical speech recognition system mainly consists of

feature extraction, training and recognition. Because of the instability of speech

signal, feature extraction of speech signal becomes very difficult. There exist

different features between each word. For each word there are differences among

different person, such as the differences between adults and children, male and

female. Even for the same person and the same word there also exists changes for

different time. Nowadays, there is several feature extraction methods used in

speech recognition systems. All of them have good performance when used in

clean condition. In the adverse condition, we still can’t find a good way in speech

recognition system.

Compared with them, human auditory system always has good

performance under clean and noisy condition. So a way solve this is to research

our auditory system and use the result in speech recognition system developed.

There are two major approaches available for feature extraction: modeling human

voice production and perception system. For the first approach, one of the most

popular features is the LPC (Linear Prediction Coefficient) feature. For the second

approach, the most popular feature is the MFCC (Mel-Frequency Cepstrum


http://en.wikipedia.org/wiki/Singing

http://en.wikipedia.org/wiki/Genetics

http://en.wikipedia.org/wiki/Band-pass_filter

http://en.wikipedia.org/wiki/Manner_of_articulation

http://en.wikipedia.org/wiki/Lip

http://en.wikipedia.org/wiki/Cheek

http://en.wikipedia.org/wiki/Soft_palate

http://en.wikipedia.org/wiki/Tongue

http://en.wikipedia.org/wiki/Vocal_tract


Coefficient) feature. In MFCC, the main advantage is that it uses Mel frequency

scaling which is very approximate to the human auditory system. Hence MFCC is

more effective than LPC.

Figure 4.4: Voice recognition algorithm classification

A. Definition of speech recognition:

Speech Recognition (is also known as Automatic Speech Recognition

(ASR), or computer speech recognition) is the process of converting a speech

signal to a sequence of words, by means of an algorithm implemented as a

computer program.

B. Types of Speech Recognition:

Speech recognition systems can be separated in several different classes by

describing what types of utterances. They have the ability to recognize. These

classes are classified as the following:

Isolated Words:

Isolated word recognizers usually require each utterance to have quiet (lack of an

audio signal) on both sides of the sample window. It accepts single words or

single utterance at a time. These systems have "Listen/Not-Listen" states, where

they require the speaker to wait between utterances (usually doing processing

during the pauses). Isolated Utterance might be a better name for this class.


Voice recognition algorithm

Training phase

Each speaker has to provide samples of their voice so that reference template model can be build

Testing phase

To ensure input test voice is matched with stored reference template model and recognition decision made accordingly


Connected Words:

Connected word systems (or more correctly 'connected utterances') are similar to

isolated words, but allows separate utterances to be 'run-together' with a minimal

pause between them.

Continuous Speech:

Continuous speech recognizers allow users to speak almost naturally, while the

computer determines the content. (Basically, it's computer dictation). Recognizers

with continuous speech capabilities are some of the most difficult to create

because they utilize special methods to determine utterance boundaries.

Spontaneous Speech:

At a basic level, it can be thought of as speech that is natural sounding and not

rehearsed. An ASR system with spontaneous speech ability should be able to

handle a variety of natural speech features such as words being run together,

"ums" and "ahs", and even slight stutters.

4.4 FEATURE EXTRACTION

Fig 4.5: Block diagram feature extraction

Step 1: Pre–emphasis

This step processes the passing of signal through a filter which emphasizes

higher frequencies. This process will increase the energy of signal at higher

frequency.



Y[n] = X[n]- a X[n-1].

a = 0.95, which make 95% of any one sample is presumed to originate from

previous sample.

Step 2: Framing

The process of segmenting the speech samples obtained from analog to

digital conversion (ADC) into a small frame with the length within the range of 20

to 40 msec. The voice signal is divided into frames of N samples. Adjacent

frames are being separated by M (M<N). Typical values used are M = 100 and N=

256.

Step 3: Windowing

A traditional method of spectral evaluation is reliable in case of stationary

signal. Nature of signal changes continuously with time. For voice reliability can

be ensured for a short time. Audio signal is continuous. Processing cannot wait for

last sample. Processing complexity increases exponentially. It is important to

retain short term features. Short time analysis is performed by windowing the

signal. Normally Hamming Window is used. The Hamming function is given by

Hamming window:

W(n) = 0.54 – 0.46 cos(2πn/L-1) 0 ≥ n≥ L-1

0 otherwise.

Step 4: Fast Fourier Transform

To convert each frame of N samples from time domain into frequency

domain. The Fourier Transform is to convert the convolution of the glottal pulse

U[n] and the vocal tract impulse response H[n] in the time domain. This statement

supports the equation below:

Y(W) = FFT [h(t)*X(t)] = H(W) *X(W)

If X (w), H (w) and Y (w) are the Fourier Transform of X (t), H (t) and Y (t)

respectively.



Step 5: Mel Filter Bank Processing

The frequencies range in FFT spectrum is very wide and voice signal does

not follow the linear scale. The bank of filters according to Mel scale as shown in

figure 4 is then performed. This figure below shows a set of triangular filters that

are used to compute a weighted sum of filter spectral components so that the

output of process approximates to a Mel scale. Each filter’s magnitude frequency

response is triangular in shape and equal to unity at the centre frequency and

decrease linearly to zero at centre frequency of two adjacent filters.

Fig.4. 6: Mel filter bank

Step 6: Discrete Cosine Transform

This process converts the log Mel spectrum into time domain using

Discrete Cosine Transform (DCT). The result of the conversion is called Mel

Frequency Cepstrum Coefficient. The set of coefficient is called acoustic vectors.

Therefore, each input utterance is transformed into a sequence of acoustic vector.

Step 7: Delta Energy and Delta Spectrum

The voice signal and the frames changes, such as the slope of a formant at

its transitions. Therefore, there is a need to add features related to the change in



cepstral features over time. 13 delta or velocity features (12 cepstral features plus

energy), and 39 features a double delta or acceleration feature are added. The

energy in a frame for a signal x in a window from time sample t1 to time sample

t2, is represented at the equation below:

Energy = ∑ X2 [t]

Procedure for forming MFCC

Fig.4.7: Flow chart for determination of MFCC

4.5 VECTOR QUANTIAZTION

Vector quantization (VQ) is a lossy data compression method based on

the principle of block coding. It is a fixed-to-fixed length algorithm. In the earlier

days, the design of a vector quantizer (VQ) is considered to be a challenging

problem due to the need for multi-dimensional integration. In 1980, Linde, Buzo,

and Gray (LBG) proposed a VQ design algorithm based on a training sequence.

The main advantage of VQ in pattern recognition is its low computational

burden when compared with other techniques such as dynamic time

warping (DTW) and hidden Markov model (HMM). A VQ is nothing more than


http://en.wikipedia.org/wiki/Hidden_Markov_model

http://en.wikipedia.org/wiki/Dynamic_time_warping

http://en.wikipedia.org/wiki/Dynamic_time_warping

http://en.wikipedia.org/wiki/Pattern_recognition

http://data-compression.com/theory.shtml#theory


an approximator. The idea is similar to that of ``rounding-off'' (say to the nearest

integer). An example of a 1-dimensional VQ is shown below:

Fig.4.8: One dimensional vector quantization

Here, every number less than -2 is approximated by -3. Every number

between -2 and 0 are approximated by -1. Every number between 0 and 2 are

approximated by +1. Every number greater than 2 are approximated by +3. Note

that the approximate values are uniquely represented by 2 bits. This is a 1-

dimensional, 2-bit VQ. It has a rate of 2 bits/dimension.

An example of a 2-dimensional VQ is shown below: Here, every pair of

numbers falling in a particular region are approximated by a star associated with

that region. Note that there are 16 regions and 16 stars -- each of which can be

uniquely represented by 4 bits. Thus, this is a 2-dimensional, 4-bit VQ. Its rate is

also 2 bits/dimension. In the above two examples, the stars are called code

vectors and the regions defined by the borders are called encoding regions. The set

of all code vectors is called the codebook and the set of all encoding regions is

called the partition of the space. The performances of VQ are typically given in

terms of the signal-to-distortion ratio (SDR):

SDR=10log10 σ2/Dave (in dB), Where σ is the variance of the source and

Dave is the average squared-error distortion. The higher the SDR the better the

performance

In verification systems two key performance measures are popular, the

false rejection rate (FRR), the number of times the true speaker is incorrectly

rejected, and false acceptance rate (FAR), the number of times an imposter

speaker is incorrectly accepted. By varying the decision threshold the FAR and

FRR will change in opposing directions. For example raising the threshold will

lower FAR but increase the FRR as true claims will start to be rejected since the



bar is raised, conversely if the threshold is lowered the FRR is reduced but FAR

will increase since not only are all true claims now accepted but more false ones

will as well. The typical operating point for the selection of the threshold is when

FAR = FRR, termed the equal error rate (EER) condition.

Fig.4.9: Two dimensional vector quantization

4.6 SOFTWARE USING: MATLAB

MATLAB is a high-level language and interactive environment for

numerical computation, visualization, and programming. MATLAB can be used

for analyzing data, developing algorithms, and creating models and applications.

The language, tools, and built-in math functions enable you to explore multiple

approaches and reach a solution faster than with spreadsheets or traditional

programming languages, such as C/C++ or Java .MATLAB has a range of

applications, including signal processing and communications, image and video

processing, control systems, test and measurement, computational finance, and

computational biology. Hence we prefer MATLAB as our software tool. More



than a million engineers and scientists in industry and academia use MATLAB,

the language of technical computing. MATLAB has built-in mathematical

functions in MATLAB to solve science and engineering problems.

MATLAB (matrix laboratory) is a numerical computing environment and fourth-

generation programming language. Developed by Math Works, MATLAB

allows matrix manipulations, plotting of functions and data, implementation

of algorithms, creation of user interfaces, and interfacing with programs written in

other languages, including C, C++, Java, and Fortran.( Cleve Moler the chairman

of computer science department started developing MATLAB in late 1970’s. Jack

Little recognized its commercial potential and joined with Moler and Steve

Banjert. They rewrote MATLAB in Cand founded mathworks in 1984)

.


http://en.wikipedia.org/wiki/Fortran

http://en.wikipedia.org/wiki/Java_(programming_language)

http://en.wikipedia.org/wiki/C%2B%2B

http://en.wikipedia.org/wiki/C_(programming_language)

http://en.wikipedia.org/wiki/User_interface

http://en.wikipedia.org/wiki/Algorithm

http://en.wikipedia.org/wiki/Function_(mathematics)

http://en.wikipedia.org/wiki/Matrix_(mathematics)

http://en.wikipedia.org/wiki/MathWorks

http://en.wikipedia.org/wiki/Fourth-generation_programming_language

http://en.wikipedia.org/wiki/Fourth-generation_programming_language

http://en.wikipedia.org/wiki/Numerical_analysis


CHAPTER 5

CONCLUSION

From the comparison study of various biometric systems we came into a

conclusion that voice recognition is best suitable for our application. As it is

having reliability, easiness of use, more social acceptance and less cost. The

devices required for the implementation of VoDAR are mat lab software and

microphone which are easily available. The Mel filter is best suitable for feature

extraction.

The Mel filter is best suitable for feature extraction. The advantage of

using Mel frequency cepstral coefficients over others are that it uses Mel

frequency scaling which are very approximate to the human auditory system.

Hence MFCC (Mel frequency cepstral coefficients) more effective than LPC

(Linear predictive coding).Vector quantization technique is more desired for

feature matching. The main advantage of VQ in pattern recognition is its low

computational burden when compared with other techniques.



BIBLIOGRAPHY

[1]. Martinez j. “Speaker identification using Mel frequency Cepstral coefficients .”ieee paper vol.2. Feb. 2012[2]. Roberto Togneri,”An Overview of Speaker Identification: Accuracy and

Robustness Issues (IEEE magazine) march 2012 ”.

[3]. Shivanker Dev Dhingra , Geeta Nijhawan, Poonam Pandit,” isolated speech

recognition using mfcc and dtw: Issue 8, August 2013”.

[4]. Ms. Arundhati S. Mehendale and Mrs. M. R. Dixit,” An International Journal

(SIPIJ) Vol.2, No.2, June 2011 :Speaker identification”

[5]. Manjot kaur gill ,reetkamal kaur ,jagdev kaur,”vector quantization based

speaker identification,international journal issue 4,2010”

[6]. Sulochana sonkamble, dr. Ravindra thool, balwant sonkamble:” survey of

biometric recognition systems And their applications: journal of theoretical and

applied information technology(2010)”.

[7]. Vibha Tiwari:”MFCC and its applications in speaker recognition international

journal on engineering technology 2010”

[8]. Priyanka Mishra, Suyash Agrawal: “Recognition of Speaker Using Mel

Frequency Cepstral Coefficient & Vector Quantization international journal on

computer applications 2010”

[9]. Proakis,” A matlab program based speech processing”.

[10]. “Speech Production” Available in

http://www.ise.canberra.edu.au/un7190/Week04Part2.html.


http://www.ise.canberra.edu.au/un7190/Week04Part2.html

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=p_Authors:.QT.Martinez,%20J..QT.&searchWithin=p_Author_Ids:38239155100&newsearch=true


APPENDIX

A1. RECORDING OF SPEECH SIGNAL

recobj=audiorecorder;% Creating an audiorecorder object

disp('start speaking');

recordblocking(recobj,5);% Call the record or recordblocking method

disp('stop speaking');

myrecording=getaudiodata(recobj);% Creating a numeric array corresponding to

%the signal data using the getaudiodata method.

plot(myrecording);

xlabel('time');

ylabel('amplitude');

A2. PRE-EMPHASIS OF SPEECH SIGNAL

x=[1,-0.95];

y=filter(x,1,myrecording);% filtering of speech signal using pre-emphasis filter.

subplot(2,2,1);

plot(y);

xlabel('sample index(n)')

ylabel('filtered output')


speech recognition

Documents

speaker voice

voice recognition

voice different

voice pitch

voice mail

ones voice

human voice

distinct voice