User Verification For Security Application.

USER VERIFICATION FOR SECURITY APPLICATION Muhammad Akmal Bin Hanzah

“I hereby declare that I have read through this report and found that it has comply the partial fulfillment for awarding the degree of Bachelor of Electrical Engineering

(Control, Instrumentation and Automation)

Signature :………. Supervisor‟s Name : Encik Ahmad Idil Bin Abdul Rahman Date : 11 MAY 2009

USER VERIFICATION FOR SECURITY APPLICATION

MUHAMMAD AKMAL BIN HANZAH

This report is submitted in partial fulfillment of requirement for the degree of Bachelor in Electrical Engineering

(Control, Instrumentation and Automation)

Faculty of Electrical Engineering Universiti Teknikal Malaysia Melaka (UTeM)

“I hereby declare that this report is a result of my own work except for the excerpts that have been cited clearly in the references.”

Signature:……… Name : Muhammad Akmal Bin Hanzah Date : 11 MAY 2009

ACKNOWLEDGEMENT

First and foremost, I thank Allah the Almighty for blessing me to complete my Project Sarjana Muda 2. I want to take this opportunity to record my utmost and sincere gratitude to my supervisor, En. Ahmad Idil Bin Abdul Rahman. Without him, I can never start work on my project and to proceed until this point of development. He has shown me guidance, important advice, and inspiration throughout my project. He has also given me knowledge essential in doing this project.

Besides, I would like to show my appreciation to my lectures, who have taught me over the years in UTeM. The have taught me the basic of Electrical Engineering, and this invaluable knowledge has provided ma a firm foundation for doing this project. Most importantly, the knowledge I required from them has prepared me for my career in the future.

Furthermore, I would like to thank my friends and fellow classmates for sharing and discussing, knowledge with me. Their support, opinion, and advised will be not forgotten.

To my beloved family, I would like to forward my obliged to them for their continuous support during my study period, their patience and benevolence. Lastly, I would like to thank everyone who has contributed during my Projek Sarjana Muda 2. your kindness and cooperation of my paperwork is much appreciated.

ABSTRACT

In daily life, human needs to communicate and change information among each other. Speech recognition has been an important subject for research, and its development has come to a stage where it has been actively and successfully applied in many industrial and consumer applications. The method used for speech recognition have since been developed and improved, with increasing accuracy and efficiency leading towards a better human-machine interface. This project is about to design and to develop a User Identification for Security Application via MATLAB programming language. User verification for security application is a project to recognize user or speaker by using their voice to apply on security system. The system importance for security purposes such as access rooms, phone banking, robots, military etc. Personal identity is usually claimed by presenting a unique personal possession such as a key, a badge, or a password.

ABSTRAK

Dalam kehidupan seharian manusia perlu berhubung dan bertukar maklumat sesama sendiri. Pengesanan suara telah menjadi subjek utama untuk kajian dan kajian tersebut telah sampai ke tahap ia telah digunakan di dalam industri dan juga aplikasi pengguna. Kaedah yang digunakan untuk pengesanan suara telah dibangunkan dan dinaiktarafkan sejajar dengan ketepatan dan kecekapan dalam memajukan antaramuka mesin-manusia. Projek ini bertujuan untuk merekabentuk dan membangunkan sebuah Aplikasi Pengenalpastian/Pengecaman Pengguna untuk Keselamatan menggunakan MATLAB. Aplikasi ini bertujuan untuk mengenalpasti pengguna melalui suara pengguna dalam sesebuah system keselamatan. Sistem amat berguna untuk tujuan keselamatan seperti akses kepada sesebuah bilik, urusan bank melalui talian, robot, ketenteraan dan sebagainya. Pengenalan diri selalunya diakui dengan menyerahkan atau menunjukkan hakmilik peribadi yang unik contohnya kunci, lencana atau kata laluan.

TABLE OF CONTENTS

CHAPTER CONTENTS PAGES

TITLE ii

ACKNOWLEDGEMENTS iv

ABSTRACT v-vi

TABLE OF CONTENTS vii-viii

LIST OF FIGURES ix

LIST OF TABLES x

1 INTRODUCTION 1 - 4

1.1 BACKGROUND 1

1.2 OBJECTIVE OF THE PROJECT 2

1.3 SCOPE OF PROJECT 3

1.3.1 Training Method 3

1.3.2 Testing Method 4

2 LITERATURE REVIEW 5 - 24

2.1 OVERVIEW 5

2.2 SPEECH RECOGNITION SYSTEM 5

2.3 SPEECH PATTERN MATCHING. 7

2.3.1 Dynamic Time Warping (DTW) 7 2.3.2 Neural Networks 9

2.3.3 Hidden Markov Model. 10

2.4 TYPES OF RECOGNIZER 12

2.5 FEATURE EXTRACTION 12

2.5.1 Linear Predictive Coding 13 2.5.2 Linear Predictive Analysis 13 2.5.3 Discrete Fourier Transform 15

2.7 BRIEF ABOUT THE COMPONENT TO USE 18

2.7.1 Max 232 18

2.7.2 Voltage Regulator 19

2.7.3 RS 232 20

2.7.4 Microcontroller 16F877 23

3 PROJECT METHODOLGY 25 - 61

3.1 INTRODUCTION 25

3.2 METHODOLOGY OF THE PROJECT 25

3.3 PROJECT FLOW 26

3.4 PROJECT IMPLEMENTATION 27

3.4.1 Software Development and 27 Implementation

3.5 SOFTWARE DEVELOPMENT 28 3.5.1 Graphical User Interface Development (GUI) 28 3.6 FLOWCHART IF THE GUI DEVELOPMENT 30 3.6.1 Front Menu 30 3.6.2 Training Method 31 3.6.3 Testing Method 33

3.7 BASIC OF CREATING MATLAB GUI 35

3.7.1 Creating The Front Menu GUI 35 3.7.1.1 To Create The Front Menu GUI 35 3.7.1.2 Initializing GUIDE (GUI Creator) 35 3.7.2 Create The Training Method GUI 38 3.7.2.1 The Function Of Each Button 39 3.7.3 Creating Testing Method GUI 44 3.7.3.1 The Function For Each Button 44

3.8 HARDWARE DEVELOPMENT 49

3.8.1 Circuit Built 49

3.8.1.1 Introduction of LED Board 49 3.8.1.2 LED Board Schematic 50 3.8.1.3 LED Board Schematic Description 51

3.8.1.4 Fabrication Method 52 3.8.1.5 The Flowchart For Creating The 55

PCB

3.8.2 Hardware Programmer 56

3.8.3 Software 57

4 ANALYSIS, RESULTS AND DISCUSSION 62 - 74

4.1 PROGRESS OF THE PROJECT 62

4.2 RESULT 63

4.2.1 Graphical User Interface (GUI) 63

4.2.1.1 Front GUI 63

4.2.1.2 Training Method GUI 64 4.2.1.3 Testing Method Interface 65

4.2.2 LED Circuit 66

4.3 DATA ANALYSIS 67

4.3.1 Accuracy Of The System 67

4.3.2 Determine Threshold 71

4.4 DISCUSSION 74

4.4.1 Software Programming 74

4.4.2 Hardware Development 74

5 CONCLUSSION AND RECOMMENDATION 75-76

5.1 CONCLUSSION 75

5.2 RECOMMENDTION 76

REFERENCES 77

LIST OF FIGURES

No Figures Page

1.1 Training Method Block Diagram 3

1.2 Testing Method Block Diagram 4

2.1 Conventional Speech Recognizer 6

2.2 Graph For Normal Sequence Pattern Set. 8

2.3 Graph For Test Sequence Pattern Set. 8

2.4 The Artificial Neuron 9

2.5 Multilayer Perceptron For The Neural Networks 10

2.6 HMM Model 11

2.7 Digital Representation Of Speech Signal 14 2.8 Graph For The Different Between Type I and II Error 17

2.9 Max 232 18

2.10 A Typical 7805 Voltage Regulator 19

2.11 RS 232 20

2.12 Signal Definition For The DCE Device 22

2.13 List Of The Conventional Usage Of Signal Names 23 2.14 Pin Diagram Of Microcontroller 16F877 23

3.1 Flowchart Of The Project Flow 26

3.2 Flowchart Of Front Menu 30

3.3 Flowchart Of Training Method GUI 31

3.4 Flowchart Of The Testing Method GUI 34

3.5 Matlab Window 35

3.6 Quick Guide Start Window 36

3.7 Front GUI 36

3.8 Training Method GUI 38

3.9 Testing Method GUI 44

3.10 LED Board Schematic 49

3.12 PCB Layout 52

3.13 The UV Unit 53

3.14 Fabricated Design 54

3.15 Flowchart Of Making The PCB 55

3.16 Serial Port Cable 57

3.17 Box Header 57

3.18 PICkit 2 Window 58

3.19 Device Configuration Area 59

3.20 VDD Target Window 59

3.21 Import HEX File Window 60

3.22 Programming Successful 61

LIST OF TABLE

No Figures Page

2.1 Comparison of Speech Recognition Systems Performance. 17

2.2 Positive voltage in 7800 series 19

LIST OF TABLE

No Content Page

A MATLAB PROGRAM 78

B HARDWARE 79

C UIC00A USB ICSP PIC PROGRAMMER 80

CHAPTER 1

INTRODUCTION

1.1 BACKGROUND

The human ear is a marvelous organ. Beyond our uniquely human ability to receive and decode spoken language, the ear supplies us with the ability to perform many diverse functions. These include, for example, localization of objects, enjoyment of music, and the identification of people by their voices. Currently, along with the efforts to develop computer procedures that understand spoken messages, there are also considerable interests in developing procedures that identity people form their voices.

User verification for security application is a project to recognized user or speaker by using their voice to apply on security system. The system importance for security purpose such as access rooms, phone banking, robots, military etc. Personal identity is usually claimed by presenting a unique personal possession such as a key, a budge, or a password.

Now days, there are several techniques to identify or verify person such as thumb print, retina, DeoxyriboNucleic Acid (DNA) and voice. As the technology moves forward, the technique to identify user also comes with alternative by using voice to verify user identity.

Among these methods, identity verification based on a person‟s voice has special advantages for practical deployment. Speech our most natural means of communication and therefore, user acceptance of the system would be very high. Advances in digital signal processors and speech technology have made possible the design of fast, cost effective, high performance speaker verification systems. These system can be integrated into providing access control for banking transactions by telephone, automatic telephone transactions by mail and credit card verification, and access to computer via modems on dial-up lines.

One of the advantages of using voice is that it leads to faster time for solution of problems. In Malaysia, speaker verification is still in its infancy stage. Speaker verification is a process where an unknown word is given as an input to a system, and the system would recognize the word and perform an output action accordingly. Thus the comparison of different algorithm has been carried out for years in various R&D laboratories in the world. Throughout the development of voice recognition different methods have been produced.

1.2 OBJECTIVE OF THE PROJECT

 To design a user / speaker verification system using speech recognition system.

 To learn about the technique use for the automated speech recognition system such as Fast Fourier Transform (FFT) and Dynamic Time Warping (DTW).

 To implement the algorithm of the systems and develop the interface by using MATLAB programming language.

1.3 SCOPE OF PROJECT

The scope of this project involve with two different module, training module and testing module. For the training module, the system will be train by the known voice to get the data and store into the reference template. For the testing module, the system will be test with unknown voice and known voice.

1.3.1 Training Method

Figure 1.1: Training method block diagram

On training module, speech input, or known voice or speaker, is converted to its digital form. This can be accomplished using a microphone which records the human voice, and using an analog-digital-converter (ADC) to convert the signal to its digital form. Next, the digitized speech samples are then processed, using digital signal processing methods, to produce speech features. Features extraction is a process of jettisoning as much as irrelevant information as possible and representing relevant data in compact and meaningful form. The method that use is Fast Fourier Transform (FFT). Then the signal will be stored in reference template or database.

Digitized speech signal

Feature extraction of speech

Reference Template Known

voice or speaker

1.3.2 Testing Method

Figure 1.2: Testing method block diagram

On testing module, speech input, known or unknown voice or speaker is converted to its digital form. This can be accomplished using a microphone which records the human voice, and using an analog-digital-converter (ADC) to convert the signal to its digital form. Next, the digitized speech samples are then processed, using digital signal processing methods, to produce speech features. Features extraction is a process of jettisoning as much as irrelevant information as possible and representing relevant data in compact and meaningful form. The method that use is Fast Fourier Transform (FFT). Then the pattern matching stage, where the unknown words feature vectors are compared with the previously stored templates to find the best match. The method use is called Dynamic Time Warping (DTW). Then the output will be the recognized user or unrecognized user determine.

Digitized speech signal

Feature extraction of speech Unknown

voice/ speaker

Recognized user or unrecognized User.

Reference Template Pattern recognition

CHAPTER 2

LITERATURE REVIEW

2.1 OVERVIEW

This chapter will discuss about source or article that related to the project. There have many sources or researches done before and from there, details about this project are known and can understand briefly about the software.

2.2 SPEECH RECOGNITION SYSTEM

The definition of speech recognition system is the ability of a computer to understand spoken words for the purpose of receiving commands and data input from the speaker. Speech recognition system also can be defined as the ability for a software application to understand spoken human commands and act on them. The challenge with voice recognition technology lies in the ability for the program to distinguish contrasting patterns in the way people speak [1].

Speech recognition (also known as automatic speech recognition or computer speech recognition) converts spoken words to machine-readable input (for example, to keypresses, using the binary code for a string of character codes). The term "voice recognition" may also be used to refer to speech recognition, but can more precisely refer to speaker recognition, which attempts to identify the person speaking, as opposed to what is being said.

Speech recognition applications include voice dialing (e.g., "Call home"), call routing (e.g., "I would like to make a collect call"), domotic appliance control and content-based

spoken audio search (e.g., find a podcast where particular words were spoken), simple data entry (e.g., entering a credit card number), preparation of structured documents (e.g., a radiology report), speech-to-text processing (e.g., word processors or emails), and in aircraft [2].

In order to understand how speech recognition system works, figure 2.1 shows the conventional block diagram for speech recognition system.

Figure 2.1: Conventional Speech Recognizer

Speech input, or human voice, is converted to its digital form. This can be accomplished using a microphone which records the human voice, and then using an analog-digital-converter (ADC) to convert the analog signal to its digital form. Thus, we will have digital data representing each level of signal at every time step.

Next, the digitized speech samples are then processed, using digital signal processing methods, to produce speech features. Feature extraction is a process of jettisoning as much irrelevant information as possible and representing relevant data in compact and meaningful form. A method of feature extraction includes Fast Fourier Transform, LPC analysis, Cepstrum, etc. These methods normally produce a sequence of feature vectors useful for the next stage, the pattern comparison stage.

Speech recognition system normally needs to be trained, where repeated utterance of known speech samples will be processed and their speech features stored in reference template. If unknown word needs to be recognized, the word is also processed but after the

Digitized Speech Signal Feature Extraction Reference Template Pattern Matching Recognized Word Input Speech

feature extraction stage, where the unknown word‟s feature vectors are compared with the previously stored templates to find the best match. The conventional method of pattern matching is called the Dynamic Time Warping (DTW) method.

2.3 SPEECH PATTERN MATHCING

One way to recognize a word is to compare it with a set of known words, or templates, stored in a machine, and to choose that word which it matches best.

2.3.1 Dynamic Time Warping (DTW)

The Dynamic Time Warping is a state of the art distance measure widely used in sequential pattern matching and it outperforms Euclidean distance in most cases because its matching is elastic and robust. The DTW is a method which two patterns of different lengths. The DTW method will map the time axis of the test patterns onto the time axis of the training pattern in such way that the resulting dissimilarity is minimized [3]. Suppose that there are 2 sequences patterns sets in graph 2.2 and graph 2.3

A

Y

A

S

S

A

Y

A

R

e

fe

re

n

c

e

te

mplate

Figure 2.2: Graph for normal sequence pattern set.

Figure 2.3: Graph for test sequence pattern set.

The line is called the warping path and each point of the line, the distance between the 2 sets. The test pattern is matched against all training patterns in the template, and the training pattern which gives the lowest accumulated distance is said to be recognized word.

2.3.2 Neural Networks

Another method of speech recognition is by using Neural Networks. A neural network is made up of computational units which mimic the biological neuron called a threshold logic unit, originally proposed by McCulloch and Pitts.

R

e

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re

n

c

e

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mplate

Test template

A

Y

A

S

The artificial neuron is shown in figure 2.4:

Here, a number of inputs (X1, X2…Xn) are weighted (W1, W2….Wn) and then summed up to produce an activation. If this activation exceeds a certain threshold, then the unit would produce a response, in the above case, either „1‟ or „0‟. The threshold function can also be a continuous function, such as the sigmoid function, where the output is then a continuous response between „1‟ and „0‟. A certain configuration of the neural units, called the multi-layer perceptron is normally used for the speech recognition [4]. Figure 2.5 show the model of multilayer perceptron for the neural networks.

W1 W2 W3 X1 X2 X3

Σ

Figure 2.4: The Artificial Neuron

p

1

1

1

2 2

1 1 1 k 1 2 1 n 1 1 1 Input X1 Input X2 Input X3 Hidden layer

Figure 2.5: Multilayer Perceptron for the Neural Networks

This neural network is trained using speech data, using supervised learning in the context of neural network, to produce excepted outputs. Then if an unknown word is given as an input to the network, the network will then output the result accordingly.

2.3.3 Hidden Markov Model.

A hidden Markov model (HMM) is a statistical model in which the system being modeled is assumed to be a Markov process with unknown parameters, and the challenge is to determine the hidden parameters from the observable parameters. The extracted model parameters can then be used to perform further analysis, for example for pattern recognition

applications. An HMM can be considered as the simplest dynamic Bayesian network.

In a regular Markov model, the state is directly visible to the observer, and therefore the state transition probabilities are the only parameters. In a hidden Markov model, the state is not directly visible, but variables influenced by the state are visible.

Each state has a probability distribution over the possible output tokens. Therefore the sequence of tokens generated by an HMM gives some information about the sequence of states.

Hidden Markov models are especially known for their application in temporal pattern recognition such as speech, handwriting, gesture recognition, part-of-speech tagging, musical

CHAPTER 2

LITERATURE REVIEW

2.1 OVERVIEW

This chapter will discuss about source or article that related to the project. There have many sources or researches done before and from there, details about this project are known and can understand briefly about the software.

2.2 SPEECH RECOGNITION SYSTEM

The definition of speech recognition system is the ability of a computer to understand spoken words for the purpose of receiving commands and data input from the speaker. Speech recognition system also can be defined as the ability for a software application to understand spoken human commands and act on them. The challenge with voice recognition technology lies in the ability for the program to distinguish contrasting patterns in the way people speak [1].

Speech recognition (also known as automatic speech recognition or computer speech recognition) converts spoken words to machine-readable input (for example, to keypresses, using the binary code for a string of character codes). The term "voice recognition" may also be used to refer to speech recognition, but can more precisely refer to speaker recognition, which attempts to identify the person speaking, as opposed to what is being said.

Speech recognition applications include voice dialing (e.g., "Call home"), call routing (e.g., "I would like to make a collect call"), domotic appliance control and content-based

spoken audio search (e.g., find a podcast where particular words were spoken), simple data entry (e.g., entering a credit card number), preparation of structured documents (e.g., a radiology report), speech-to-text processing (e.g., word processors or emails), and in aircraft [2].

In order to understand how speech recognition system works, figure 2.1 shows the conventional block diagram for speech recognition system.

Figure 2.1: Conventional Speech Recognizer

Speech input, or human voice, is converted to its digital form. This can be accomplished using a microphone which records the human voice, and then using an analog-digital-converter (ADC) to convert the analog signal to its digital form. Thus, we will have digital data representing each level of signal at every time step.

Next, the digitized speech samples are then processed, using digital signal processing methods, to produce speech features. Feature extraction is a process of jettisoning as much irrelevant information as possible and representing relevant data in compact and meaningful form. A method of feature extraction includes Fast Fourier Transform, LPC analysis, Cepstrum, etc. These methods normally produce a sequence of feature vectors useful for the next stage, the pattern comparison stage.

Speech recognition system normally needs to be trained, where repeated utterance of known speech samples will be processed and their speech features stored in reference template. If unknown word needs to be recognized, the word is also processed but after the

Digitized Speech Signal

Feature Extraction

Reference Template Pattern Matching

Recognized Word Input

feature extraction stage, where the unknown word‟s feature vectors are compared with the previously stored templates to find the best match. The conventional method of pattern matching is called the Dynamic Time Warping (DTW) method.

2.3 SPEECH PATTERN MATHCING

One way to recognize a word is to compare it with a set of known words, or templates, stored in a machine, and to choose that word which it matches best.

2.3.1 Dynamic Time Warping (DTW)

The Dynamic Time Warping is a state of the art distance measure widely used in sequential pattern matching and it outperforms Euclidean distance in most cases because its matching is elastic and robust. The DTW is a method which two patterns of different lengths. The DTW method will map the time axis of the test patterns onto the time axis of the training pattern in such way that the resulting dissimilarity is minimized [3]. Suppose that there are 2 sequences patterns sets in graph 2.2 and graph 2.3

A

Y

A

S

S

A

Y

A

R

e

fe

re

n

c

e

te

mplate

Figure 2.2: Graph for normal sequence pattern set.

Figure 2.3: Graph for test sequence pattern set.

The line is called the warping path and each point of the line, the distance between the 2 sets. The test pattern is matched against all training patterns in the template, and the training pattern which gives the lowest accumulated distance is said to be recognized word.

2.3.2 Neural Networks

Another method of speech recognition is by using Neural Networks. A neural network is made up of computational units which mimic the biological neuron called a threshold logic unit, originally proposed by McCulloch and Pitts.

R

e

fe

re

n

c

e

te

mplate

Test template

A

Y

A

S

The artificial neuron is shown in figure 2.4:

Here, a number of inputs (X1, X2…Xn) are weighted (W1, W2….Wn) and then summed up to produce an activation. If this activation exceeds a certain threshold, then the unit would produce a response, in the above case, either „1‟ or „0‟. The threshold function can also be a continuous function, such as the sigmoid function, where the output is then a continuous response between „1‟ and „0‟. A certain configuration of the neural units, called the multi-layer perceptron is normally used for the speech recognition [4]. Figure 2.5 show the model of multilayer perceptron for the neural networks.

W1 W2 W3 X1

X2 X3

Σ

Figure 2.4: The Artificial Neuron

p

1

1

1

2 2

1

1

1

k

1

2

1

n

1

1

1

Input X1

Input X2

Input X3

Hidden layer

Figure 2.5: Multilayer Perceptron for the Neural Networks

This neural network is trained using speech data, using supervised learning in the context of neural network, to produce excepted outputs. Then if an unknown word is given as an input to the network, the network will then output the result accordingly.

2.3.3 Hidden Markov Model.

A hidden Markov model (HMM) is a statistical model in which the system being modeled is assumed to be a Markov process with unknown parameters, and the challenge is to determine the hidden parameters from the observable parameters. The extracted model parameters can then be used to perform further analysis, for example for pattern recognition applications. An HMM can be considered as the simplest dynamic Bayesian network.

In a regular Markov model, the state is directly visible to the observer, and therefore the state transition probabilities are the only parameters. In a hidden Markov model, the state

is not directly visible, but variables influenced by the state are visible.

Each state has a probability distribution over the possible output tokens. Therefore the sequence of tokens generated by an HMM gives some information about the sequence of states.

Hidden Markov models are especially known for their application in temporal pattern recognition such as speech, handwriting, gesture recognition, part-of-speech tagging, musical score following, partial discharges and bioinformatics [2]. Figure 2.6 shows the HMM model.