Project Report-final yr (6months)-eshan

Project Report On

VOICE IDENTITY SECURITY SYSTEM SUBMITTED FOR THE PARTIAL FULFILLMENT OF AWARD OF BACHELOR OF TECHNOLOGY IN INFORMATION TECHNOLOGY& ENGINEERING (U.P. TECHNICAL UNIVERSITY, LUCKNOW)

Under the Guidance of Ms Jyoti Bajpai Department of CS/IT G.L.A.I.T.M, Mathura.

DEPARTMENT OF COMPUTER SCIENCE &ENGINEERING

G.L.A. INSTITUTE OF TECHNOLOGY & MANAGEMENT

(Affiliated to U.P. Technical University, Lucknow) NH-2, MATHURA(U.P.) (2010-11) i

DECLARATION We hereby declare that this submission is our own work of six months and that, to the best of our knowledge and belief, it contains no material previously published or written by another person nor material which to a substantial extent has been accepted for the award of any other degree or diploma of the university or other institute of higher learning, except where due acknowledgment has been made in the text.

TEAM MEMBERS: Neha Bansal Eshan Gupta Desh Deepak Narapendra Kumar Trivedi

IT FINAL YEAR IT FINAL YEAR IT FINAL YEAR IT FINAL YEAR

(0706313036) (0706313017) (0706313015) (0706313033)

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CERTIFICATE

This is to certify that Project Report entitled “VOICE IDENTITY SECURITY SYSTEM “. Which is submitted by GROUP-C4 in partial fulfillment of the requirement for the award of degree B. Tech. in Department of Computer Science & Engineering of U. P. Technical University, is a record of the candidate own work carried out by him under my/our supervision. The matter embodied in this thesis is original and has not been submitted for the award of any other degree.

Dr. Charul Bhatnagar (Head of Department)

Mr. Dilip Sharma (Project In-charge)

Ms. Jyoti Bajpai (Project Guide) Department of CSE G.L.A.I.T.M, Mathura.

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ACKNOWLEDGEMENT It gives us a great sense of pleasure to present the report of the B. Tech Project undertaken during B. Tech. Final Year. We owe special debt of gratitude to our project guide Ms. Jyoti Bajpai ,Department of Computer Science & Engineering, GLAITM,MATHURA for her constant support and guidance throughout the course of our work. Her sincerity, thoroughness and perseverance have been a constant source of inspiration for us. It is only her cognizant efforts that our endeavors have seen light of the day. We also do not like to miss the opportunity to acknowledge the contribution of Mr.Manish Kashyap & all faculty members of the department for their kind assistance and cooperation during the development of our project. Last but not the least, we acknowledge our friends for their contribution in the completion of the project.

Signature:

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ABSTRACT This report is an attempt to unravel the classical problem of voice recognition. It has been an area of research for quite a long time now. “ Voice Identity Security system “ Recognizes who is speaking by using the speaker-specific information included in speech waves to verify identities being claimed by people accessing systems, that is, it enables access control of various services by voice. This technique makes it possible to use the speaker’s voice to verify their identity & control access to services. This project has been implemented in MATLAB by computing MFCC (Mel Frequency Cepstral Coefficients). The voice has been quantized by using Vector Quantization. The speaker’s voice has been recorded and MFCC for the voice are computed. The speaker is then identified using those MFCC’s and the system will work accordingly.

Our research primarily concentrates on the identification task. The aim is to recognize unknown speaker from a set of known speakers. In this way, speaker recognition technology is expected to create new services that will make our daily lives more convenient.

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TABLE OF CONTENTS Page DECLARATION CERTIFICATE ACKNOWLEDGEMENTS ABSTRACT LIST OF TABLES LIST OF FIGURES

ii iii iv v vi viii

CHAPTER 1: INTRODUCTION, OBJECTIVE AND HISTORY

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1.1 Voice Recognition 1.1.1 Speaker Recognition 1.1.2 Verification and Identification 1.1.3 Variants of Voice Recognition 1.2 History 1.3 Objective 1.4 About MATLAB 1.4.1 Features of MATLAB 1.4.1.1 Creating function m-files with a plain text editor 1.4.1.2 Function Definition 1.4.1.3 Input and output parameters 1.4.1.4 Comments statements 1.4.1.5 Local Variables 1.5 Voice Recognition Technology 1.5.1 Technology for Speaker Recognition 1.6 Applications of VOICE RECOGNITION 1.6.1 Health Care 1.6.2 Military 1.6.3 Telephony and other domains 1.6.4 People with disabilities CHAPTER 2: FEASIBILITY STUDY, REQUIREMENT ANALYSIS

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2.1 Software Development Life Cycle 2.2 Feasibility Study 2.2.1 Three phases of Feasibility Study 2.2.1.1 Technical Feasibility 2.2.1.2 Economical Feasibility 2.2.1.3 Operational Feasibility 2.2.2 Steps involved in Feasibility Study 2.3 Requirement Analysis 2.3.1 Software Requirement Specification 2.3.2 Analysis Methodology 2.3.3Types of Information Needed 2.4 System Requirements 2.4.1 Software requirements 2.4.2 Hardware Requirements CHAPTER 3: SYSTEM ANALYSIS & SYSTEM DESIGN 3.1 System Analysis 3.1.1 Specification of Project

18-25

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3.2 System Design 3.2.1 Design Concept 3.3 Data Flow Diagrams (DFD) 3.3.1 Data Flow 3.3.2 Developing Data-Flow Diagram 3.3.2.1 Top-Down Approach. 3.3.3 Data Flow Diagram Levels 3.3.3.1 Context Level Diagram. 3.3.3.2 Level 1 (High Level Diagram). CHAPTER 4: DETAILED DESIGN 4.1 Detailed design 4.1.1 Enrollment 4.1.2 Feature Extraction 4.1.2.1 Frame Blocking 4.1.2.2 Windowing 4.2 Speaker Recognition using Vector Quantization CHAPTER 5: INTERFACE OF USER 5.1 Snapshots 5.1.1 G.U.I 5.1.2 New User 5.1.3 Home 5.1.4 Already Existing User 5.1.5 Database CHAPTER 6: IMPLEMENTATION AND CONCLUSION 6.1 Implementation 6.2 Conclusions 6.2.1 Scope of Improvements APPENDIX BIBLIOGRAPHY

26-28

29-31

32-33

34-43 44

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LIST OF FIGURES

Number Figure 3.1

Description Zero Level DFD

Figure 3.2

1st Level DFD

Figure 3.3

2nd Level DFD

Figure 3.4

2nd Level DFD

Figure 3.5

2nd Level DFD

Figure 6.1

G.U.I

Figure 6.2

New User

Figure 6.3

Home

Figure 6.4

Existing User

Figure 6.5

Database

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CHAPTER 1 INTRODUCTION

1.1 VOICE RECOGNITION Voice recognition is the combination of two concepts Speaker recognition and Speech Recognition. There is a difference between speaker recognition (recognizing who is speaking) and speech recognition (recognizing what is being said) [5]. These two terms are frequently confused, as is voice recognition. Voice recognition is combination of the two where it uses learned aspects of a speakers voice to determine what is being said 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. In addition, there is a difference between the act of authentication (commonly referred to as speaker verification or speaker authentication) and identification. 1.1.1 Speaker recognition Speaker recognition is the process of automatically recognizing who is speaking on the basis of individual information included in speech waves[4].Speaker recognition is the process of automatically recognizing who is speaking by using the speaker-specific information included in speech waves to verify identities being claimed by people accessing systems; that is, it enables access control of various services by voice Speaker recognition has a history dating back some four decades and uses the acoustic features of speech that have been found to differ between individuals[5]. These acoustic patterns reflect both anatomy (e.g., size and shape of the throat and mouth) and learned Project Report (Voice Identity Security System)

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behavioral patterns (e.g., voice pitch, speaking style). Simply Speaker recognition is usually a general name referring to two different subtasks: speaker identification (SI) and speaker verification (SV). Our research primaly concentrates on the identification task. The aim in SI is to recognize the unknown speaker from a set of known speakers (closed-set SI). In this way, speaker recognition technology is expected to create new services that will make our daily lives more convenient.

1.1.2 Verification and Identification There are two major applications of speaker recognition technologies and methodologies. If the speaker claims to be of a certain identity and the voice is used to verify this claim, this is called verification or authentication. On the other hand, identification is the task of determining an unknown speaker's identity. In a sense speaker verification is a 1:1 match where one speaker's voice is matched to one template (also called a "voice print" or "voice model") whereas speaker identification is a 1:N match where the voice is compared against N templates. From a security perspective, identification is different from verification. For example, presenting your passport at border control is a verification process - the agent compares your face to the picture in the document. Conversely, a police officer comparing a sketch of an assailant against a database of previously documented criminals to find the closest match(es) is an identification process. In forensic applications, it is common to first perform a speaker identification process to create a list of "best matches" and then perform a series of verification processes to determine a conclusive match.

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Speaker identification is the process of determining which registered speaker provides a given utterance. Speaker verification, on the other hand, is the process of accepting or rejecting the identity claim of a speaker. Most applications in which a voice is used as the key to confirm the identity of a speaker are classified as speaker verification. 1.1.3 Variants of speaker recognition Each speaker recognition system has two phases: Enrollment and Verification. During enrollment, the speaker's voice is recorded and typically a number of features are extracted to form a voice print, template, or model. In the verification phase, a speech sample or "utterance" is compared against a previously created voice print. For identification systems, the utterance is compared against multiple voice prints in order to determine the best match(es) while verification systems compare an utterance against a single voice print. Because of the process involved, verification is faster than identification.

1.2 HISTORY Although the largest strides in the development of voice recognition technology have occurred in the past two decades, this technology really began with Alexander Graham Bell's inventions in the 1870s. By discovering how to convert air pressure waves (sound) into electrical impulses, he began the process of uncovering the scientific and mathematical basis of understanding speech. In the 1950s, Bell Laboratories developed the first effective speech recognizer for numbers. In the 1970s, the ARPA Speech Understanding Research project developed the technology further - in particular by recognizing that the objective of automatic speech recognition is the understanding of speech not merely the recognition of words. Voice Identity Security System

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By the 1980s, two distinct types of commercial products were available. The first offered speaker-independent recognition of small vocabularies. It was most useful for telephone transaction processing. The second, offered by Kurzweil Applied Intelligence, Dragon Systems, and IBM, focused on the development of large-vocabulary voice recognition systems so that text documents could be created by voice dictation. Over the past two decades, voice recognition technology has developed to the point of real-time, continuous speech systems that augment command, security, and content creation tasks with exceptionally high accuracy [5].

1.3 OBJECTIVE:  Developing a project which can record the voice of the speaker and identifies it later.  Develop a project which can be used in areas where security is required based on the voice.  Develop a project which can reduce the strain from the user to minimum using his speech.  Develop a project which makes the system completely speech enabled.

1.4 ABOUT MATLAB: MATLAB is a high-level scientific and engineering programming environment which provides many useful capabilities for plotting and visualizing data and has an extensive library of built-in functions for data manipulation [3]. MATLAB integrates computation, graphics, and programming in a flexible, open environment. Known for its highly optimized matrix and vector calculations, MATLAB offers you an intuitive language for

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expressing problems and their solutions both mathematically and visually. Typical uses include: 

Numeric computation and algorithm development



Symbolic computation (with the built-in Symbolic Math Toolbox)



Modeling, simulation, and prototyping



Data analysis and signal processing



Engineering graphics and scientific visualization

Application development, including graphical user interface building MATLAB

has

evolved over a period of years with input from many users. In university environments, it is the standard instructional tool for introductory and advanced courses in mathematics, engineering, and science. In industry, MATLAB is the tool of choice for high productivity research, development, and analysis.

MATLAB is built around the MATLAB language, sometimes called M-code or simply M. The simplest way to execute M-code is to type it in at the prompt, >> , in the Command Window, one of the elements of the MATLAB Desktop. In this way, MATLAB can be used as an interactive mathematical shell. Sequences of commands can be saved in a text file, typically using the MATLAB Editor, as a script or encapsulated into a function, extending the commands available. MATLAB is helpful in FACE RECOGNITION because MATLAB has pre-install patch’s which help’s in importing their different properties easily for developing a well documented FACE RECOGNITION .

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1.4.1 FEATURES OF MATLAB: 1.4.1.1 Creating function m-files with a plain text editor MATLAB m-files must be plain text files, i.e. files with none of the special formatting characters included by default in files created by word-processors. Most word-processors provide the option of saving the file as plain text, (look for a ``Save As...'' option in the file menu) [3]. A word-processor is overkill for creating m-files, however, and it is usually more convenient to use a simple text editor, or a ``programmer's editor''. For most types of computers there are several text editors (often as freeware or shareware). Usually one plain text editor is included with the operating system. 1.4.1.2 Function Definition The first line of a function m-file must be of the following form. function [output_parameter_list]= function_name(input_parameter_list)

The first word must always be ``function''. Following that, the (optional) output parameters are enclosed in square brackets [ ]. If the function has no output_parameter_list the square brackets and the equal sign are also omitted. The function_name is a character string that will be used to call the function. The function_name must also be the same as the file name (without the ``.m'') in which the function is stored. In other words the MATLAB function, ``foo'', must be stored in the file, ``foo.m''. Following the file name is the (optional) input_parameter_list.

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1.4.1.3 Input and Output parameters The input_parameter_list and output_parameter_list are comma-separated

lists of

MATLAB variables. Unlike other languages, the variables in the input_parameter_list should never be altered by the statements inside the

function. Expert MATLAB

programmers have ways and reasons forviolating that principle, but it is good practice to consider the input variables to be constants that cannot be changed. The separation of input and output variables helps to reinforce this principle. 1.4.1.4 Comment statements MATLAB comment statements begin with the percent character, %. All from the % to the end of the line are treated as a comment. The %

characters

character does not

need to be in column 1.

1.4.1.5 Local Variables Unless explicitly declared to be global variables, all variables appearing in a MATLAB function are local to that function.

1.5 VOICE RECOGNITION TECHNOLOGY The term "voice recognition" is sometimes used to refer to speech recognition where the recognition system is trained to a particular speaker - as is the case for most desktop recognition software, hence there is an element of speaker recognition, which attempts to identify the person speaking, to better recognize what is being said. Speech recognition is a broad term which means it can recognize almost anybody's speech - such as a callcentre system designed to recognize many voices. Voice recognition is a system trained to a particular user, where it recognizes their speech based on their unique vocal sound.

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1.5.1 Technology for Speaker Recognition The various technologies used to process and store voice prints include frequency estimation, hidden Markov models, Gaussian mixture models, pattern matching algorithms, neural networks, matrix representation,Vector Quantization and decision trees. Some systems also use "anti-speaker" techniques, such as cohort models, and world models [1]. Ambient noise levels can impede both collection of the initial and subsequent voice samples. Noise reduction algorithms can be employed to improve accuracy, but incorrect application can have the opposite effect. Performance degradation can result from changes in behavioural attributes of the voice and from enrolment using one telephone and verification on another telephone ("cross channel"). Integration with two-factor authentication products is expected to increase. Voice changes due to ageing may impact system performance over time. Some systems adapt the speaker models after each successful verification to capture such long-term changes in the voice, though there is debate regarding the overall security impact imposed by automated adaptation. Capture of the biometric is seen as non-invasive. The technology traditionally uses existing microphones and voice transmission technology allowing recognition over long distances via ordinary telephones (wired or wireless).

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1.6 APPLICATIONS OF VOICE RECOGNITION 1.6.1 Health care In the health care domain, even in the wake of improving speech recognition technologies, medical transcriptionists (MTs) have not yet become obsolete. The services provided may be redistributed rather than replaced. Speech recognition is used to enable deaf people to understand the spoken word via speech to text conversion, which is very helpful. 1.6.2 Military Substantial efforts have been devoted in the last decade to the test and evaluation of speech recognition in fighter aircraft. Of particular note are the U.S. program in speech recognition for the Advanced Fighter Technology Integration (AFTI)/F-16 aircraft (F-16 VISTA), the program in France on installing speech recognition systems on Mirage aircraft, and programs in the UK dealing with a variety of aircraft platforms. In these programs, speech recognizers have been operated successfully in fighter aircraft with applications including: setting radio frequencies, commanding an autopilot system, setting steer-point coordinates and weapons release parameters, and controlling flight displays. Generally, only very limited, constrained vocabularies have been used successfully, and a major effort has been devoted to integration of the speech recognizer with the avionics system.

1.6.3 Telephony and other domains ASR in the field of telephony is now commonplace and in the field of computer gaming and simulation is becoming more widespread. Despite the high level of integration with word processing in general personal computing, however, ASR in the field of document production has not seen the expected increases in use.The improvement of mobile Voice Identity Security System

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processor speeds made feasible the speech-enabled Symbian and Windows Mobile Smartphones. Speech is used mostly as a part of User Interface, for creating pre-defined or custom speech commands. Leading software vendors in this field are: Microsoft Corporation (Microsoft Voice Command), Nuance Communications (Nuance Voice Control), Vito Technology (VITO Voice2Go), Speereo Software (Speereo Voice Translator) and SVOX. 1.6.4 People with disabilities People with disabilities can benefit from speech recognition programs. Speech recognition is especially useful for people who have difficulty using their hands, ranging from mild repetitive stress injuries to involved disabilities that preclude using conventional computer input devices. In fact, people who used the keyboard a lot and developed RSI became an urgent early market for speech recognition. Speech recognition is used in deaf telephony, such as voicemail to text, relay services, and captioned telephone. Individuals with learning disabilities who have problems with thought-to-paper communication (essentially they think of an idea but it is processed incorrectly causing it to end up differently on paper) can benefit from the software.

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CHAPTER 2 FEASIBILITY STUDY, REQUIREMENT ANALYSIS

2.1 SOFTWARE DEVELOPMENT LIFE CYCLE Since the inception of this project all software engineering principles have been followed. This project has passed through all the stages of software development life cycle (SDLC). A development process consist of various phases, each phase ending with a defined output. The main reason for following the SDLC process is that it breaks the problem of developing software into successfully performing a set of phases, each phase handling a different concern of software development. Object technologies lead to reuse and reuse (of program components) lead to faster software development and higher quality programs. Object oriented software is easy to maintain because its structure is inherently decoupled. In addition, object oriented systems are easier to adopt and easier to scale. The Object Oriented process moves through an evolutionary spiral that starts with customer satisfaction. It is here that the problem domain is defined and that basic problem classes are identified. Planning establishes a foundation for the Object Oriented Project Plan.

2.2 FEASIBILTY STUDY: It is feasible because it is being frequently used in various areas like military, telephone, healthcare etc. It is also

used by topmost industries for the recognition of their

employees in their attendance process. So it is feasible and can be completed in given period. A Real-Time Voice Recognition Security System can be developed using the different algorithm.

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2.2.1 THREE PHASES OF FEASIBILITY STUDY 2.2.1.1 Technical Feasibility: It involves determining whether or not a system can actually be constructed to solve the problem at hand. The technical issues raised during the feasibility stage of investigation are related to achievability of project’s goal and possibility of completion of project.

2.2.1.2 Economical Feasibility: This feasibility deals with the cost/benefit analysis. A number of intangible benefits like user friendliness, robustness and security were pointed out. The cost that will be incurred upon the implementation of this project would be quite nominal.

2.2.1.3 Operational Feasibility: The developed system will be very reliable and user friendly. All the features and operations that we will implement in our project are possible to implement and thus feasible. This will facilitate easy use and adoptability of the system. With the use of menus, and proper validation required it become fully understandable to the common user and operational with the user.

2.2.2 STEPS INVOLVED IN THE FEASIBILITY ANALYSIS Feasibility is carried out in the following steps: Form a project team and appoint a project leader: First of all project management of the organization forms separate teams for independent project team comprises of one or system analyst and programmers with a project leader. The project leader is responsible for planning and managing the development activities of the system. Starts preliminary investigation: The system analyst of each project team starts preliminary investigation through different fact techniques. Voice Identity Security System

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Prepare the current system flow chart: After preliminary investigation; the analysts prepare the system flowchart of the current system. These charts describe the general working of the system in graphical way. Determine objective of the proposed system: The major objectives of the proposed system are listed by each analyst and are discussed in the current system.

Describe the deficiencies of the proposed system: On study the current system flowchart, the analysts prepare their system flowchart; the analysts prepare their system flowchart. Systems flowcharts of the proposed system are compared with of the current system. Prepare the proposed system flow chart: After determining the major objectives of the proposed system; the analysts prepare their system flowchart. Systems flowcharts of the proposed system are compared with of the current system. Determining the technical feasibility: The existing computer systems (hardware\software) of the concerned department are identified and their technical specifications are noted down. The analyst decides whether the existing systems are sufficient for the technical requirement of the proposed system or not. Determine the operational feasibility: After determine the economic feasibility, the analysts identify the responsible users of the system and hence determine the operational feasibility of the project. Presentation of feasibility analysis: During the feasibility study, the analysts also keep on the feasibility report. At the end feasibility analysis report is given to the management along the oral presentation. Voice Identity Security System

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Feasibility Analysis report: Feasibility analysis report is formal document for management use and is prepared for system analyst during or after feasibility study. Presentation of feasibility analysis: During the feasibility study, the analysts also keep on the feasibility report. At the end feasibility analysis report is given to the management along the oral presentation. Feasibility analysis report: Feasibility analysis report is formal document for management use and is prepared for system analyst during or after feasibility study. This report generally contains the following sections.

Covering letter: It is formally presents the report with brief description of the project problem along with recommendation to be considered. Table of content: It lists the section of feasibility study report along with their page number. Overview: It presents the overview of the project problem along with the purpose and scope of the project. Description of the existing system: A brief description of the existing system along with the purpose and scope of the project. System requirement: The system requirements, which are either derived from the existing system or from the discussion with the users, are presented in this section. Description of proposed system: It presents a general description of the proposed system, highlighting its role in solving the problem. A description of output reports to be generated by the system is also represented in the desired formats. Voice Identity Security System

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Development plan: It present a detailed plan with the starting and completion dates for different phases of SDLC. Complimentary planes also needed for hardware and software evaluation, purchase and installation. Technical feasibility finding: It presents the finding of technical feasibility study along with recommendation. Costs and benefits: The detailed findings of cost and benefits analysis are presented in this section. The saving and benefits are highlighted to justify the economic feasibility of this project. Operational feasibility finding: It presents the finding of operational feasibility along with the human resource requirements to implement the system.

2.3 REQUIREMENT ANALYSIS A requirement is a condition or capability that must be met or possessed by a system to satisfy a contract, standard, specification or other formally imposed specification of the client. This phase ends with the Software Requirements Specifications (SRS). The SRS is a document that completely describes what the proposed software should do without describing how the software will do it.

2.3.1 SOFTWARE REQUIREMENTS SPECIFICATIONS System Analysis is a technique for carrying out system requirement & project management using structured analysis for specifying both manual & automated system. In system analysis the focus is on inquiring of current organizational environment, Voice Identity Security System

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defining the system requirement, making recommendation for system improvement and determining the feasibility of system.

2.3.2 Analysis Methodology: A complete understanding of requirement is essential for success of a project. This is done by gathering information, the approach and manner in which sensitivity, common sense and knowledge of what and when to gather and what to use in securing information. There are various tools for gathering during the phase of system analysis. The phases are:1. Familiarity with the present through available documentation, such as procedure manuals, document and their flow, interviews of user staff and on site observation. 2. Defining of decision making associated with managing the system. This is important for determining what information is required of the system conduction interview clarifies the decision point and how decision made in user area. 3. Once decision point is identified, a database may be conduct to define the information requirement. The information gathered is analyzed and documented. Discrepancies between decision system and information gathered from the information system are identified. This concludes the analysis and sets the stage for system design.

2.3.3 Type of Information Needed: Organization based information deals with policies, objectives, goals and structure. User based information focuses on information requirement. Work based information addresses the work flow, method & procedure and workstation. We are interested in what happened to data through various point in system.

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2.4 SYSTEM REQUIREMENTS: 2.4.1 SOFTWARE REQUIREMENTS: 

MATLAB 7.0.4 / MATLAB 7.6.0(R2008a ) for coding.



Microsoft Access for database.



Microsoft Word is used for documentation.

2.4.2 HARDWARE REQUIREMENTS: 

Processor: PC with a Pentium IV-class processor, 600 MHz, Recommended: Pentium IV-class, 1.63 GHz.



O.S. - Windows XP/Vista/7 (1 GB RAM).



Hard Disk Space: 20 GB on system drive, 10 GB for development environment.



Good Quality Headphone with mic.



USB port.

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CHAPTER 3 SYSTEM ANALYSIS & SYSTEM DESIGN

Requirement analysis defines “WHAT” the system should do; design tells ‘HOW’ to do it. This is the simplest way to defines system design. Any design has to be constantly evaluated to ensure that it meet its requirements, is practical and workable in the given environment. If there are number of alternatives, then all alternatives must be evaluated and the best possible solution must be implemented.

3.1 SYSTEM ANALYSIS System Analysis is a term used to describe the process of calculating and analyzing facts in respect of existing operation of the prevailing situations that an effective computerized system may be designed and implemented if provided feasible. This is required in order to understand the problem that has to be solved. The problem may be of any kind like computerizing an existing system or developing an entirely new system or it can be a combination of two. Basically system analysis is used to describe the process of calculating and analyzing facts related to the existing operations of the prevailing situation, so that an effective and accurate computerized system may be designed and implemented if feasible. This is required in order to understand the problem the problem that has to be solved. To solve the problem in actual sense is not the aim of designing phase, but to see how the problem can be solved. For this the logical model of the system is required, providing the way to solve the problem and achieving the desired goal. The logical view of the system Voice Identity Security System

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is provided to the developer and user for decision making such that developer can feel ease in designing the system.

3.1.1 SPECIFICATION OF PROJECT The proposed system should have following features: 1. It should be able to store voices in “.wav” format. 2. It should be able to store usernames in database. 3. It should provide the option for existing and new user. 4. It should have the ability of processing voice prints. 5. It should closely match the voices. 6. It should recognize speech up to a reasonable extent. 7. It should provide proper guidance to the user to use it. 8. It should give fast results.

3.2 SYSTEM DESIGN System Design is the technique of creating a system that takes into notice such factors such as needs, performance levels, database design, hardware specifications, and data management. It is the most important part in the development part in the development of the system, as in the design phase the developer brings into existence the proposed system the analyst through of in the analysis phase.

3.2.1 DESIGN CONCEPT Software design sites at the technical kernel of software engineering and is applied regardless of the software process model that is used. After software requirements have been analyzed and specified. Software design is the first of three technical activitiesdesigns, code generation and test-that are required to build and verify the software. Each activity transforms information in a manner that utility results in validated computer Voice Identity Security System

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software. The design transforms the information domain model created during analysis into the data structure that will be required to implement the software. The data objects and relationship diagram and the detailed data content depicted in the data dictionary provide the basis for the design activity. As aforesaid “Design” is that phase of software engineering that tells all about the completion of a project or complete failure. In our project Face Recognition System we have spent maximum time on Image preprocessing & processing. Now we are ready with processed images so as to make it easier for the user to match images. Also data flow diagrams for the project has been developed. While developing this project we have gone through various angles of images. The training data base structures are well defined with complete description of images about the used. Another part which took most of our consideration is that we decided to create the user input for directly giving path of images in the dialog box and then executing each of them. The architectural design defined the relationship between major structure elements of the software, the “design patterns” that can be used to achieve the requirements that have been defined for the system and the constraints that affect the way in which architectural design pattern can be applied. The interface design describes how the software communicates with in itself, with systems that interoperate with it, and with humans who use it .An interface applies a flow of information and a specific type of behavior. Design is the phase where quality is fostered in website designing. Design provides us with representations of software that can be assessed for quality. Design is the only way that we can accurately translate a customer’s requirement into a finished software

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product or systems. Website design serves as the foundation of the software support steps that follow.

3.3 DATA FLOW DIAGRAMS (DFD) The Voice Recognition Security System performs three tasks 

Enrollment



Feature Extraction



Verification

The Enrollment is done to enroll the user into database and record his voice into folder. The Feature Extraction phase will extract the measurable features from the stored voice for verification. The Verification in performed on the basis of the extracted features. The verified user is able to perform the task by training the speech recognition engine.

3.3.1 DATA FLOW Dataflow diagram (DFD) graphically shows the relationship between the process and data. It is used to describe and analyze the moment of data through a system, manual or automated. The focus of the data flow in the system is between the process and in and out of data stores. This is a central tool and basis from which other components are developed. The system models are termed as Data Flow Diagram. It is common practice to draw a context-level data flow diagram first which shows the interaction between the system and outside entities. The DFD is designed to show how a system is divided into smaller portions and to highlight the flow of data between those parts. This context-level data-flow diagram is then "exploded" to show more detail of the system being modeled.

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DFD is also a virtually designable diagram that technically or diagrammatically describes the inflow and outflow of data or information that is provided by the external entity. DFD show the flow of data from external entities into the system. Show how the data moved from one process to another, as well as its logical storage. There are only four symbols: SQUARES representing external entities, which are sources or destinations of data, ROUNDED RECTANGLES representing process, which take data as input, process it, and give the output. ARROW representing the data flows, which can either, is electronic data or physical items. OPEN-ENDED RECTANGLES representing data stores, including electronic stores such as database or XML files and physical stores such as filling cabinets or stakes of paper.

3.3.2 DEVELOPING DATA-FLOW DIAGRAM 3.3.2.1 Top-Down Approach: The system designer makes "a context level DFD" or Level 0, which shows the "interaction" (data flows) between "the system" (represented by one process) and "the system environment" (represented by terminators). The system is "decomposed in lower-level DFD (Level 1)" into a set of "processes, data stores, and the data flows between these processes and data stores". Each process is then decomposed into an "even-lower-level diagram containing its sub processes". This approach "then continues on the subsequent sub processes", until a necessary and sufficient level of detail is reached which is called the primitive process

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3.3.3 DATA FLOW DIAGRAM LEVELS 3.3.3.1 Context Level Diagram: This level shows the overall context of the system and its operating environment and shows the whole system as just one process. It does not usually show data stores, unless they are "owned" by external systems, e.g. are accessed by but not maintained by this system, however, these are often shown as external entities.

3.3.3.2 Level 1 (High Level Diagram): This level (Level 1) shows all processes at the first level of numbering, data stores, external entities and the data flows between them. The purpose of this level is to show the major high-level processes of the system and their interrelation. A process model will have one, and only one, level-1 diagram. A level-1 diagram must be balanced with its parent context level diagram, i.e. there must be the same external entities and the same data flows, these can be broken down to more detail in the level1.

Figure 3.1 Zero Level DFD

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Figure 3.2 1st Level DFD

Figure 3.3 2nd Level DFD

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Figure 3.4 2nd Level DFD

Figure 3.5 2nd Level DFD

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CHAPTER 4 DETAILED DESIGN

4.1 DETAILED DESIGN The detailed design of the project is done after the initial design of the project. Once the DFD’s are prepared the detailed design of the various modules of the project is carried out. The Voice Recognition Security System has the following four phases 

Enrollment



Feature Extraction



Verification

4.1.1 Enrollment: In this the user enrolls himself into the database by giving his userrname. The username is stored in Microsoft Access database named “sr” in the table “ab1”. 4.1.2 Feature Extraction: In this phase the measurable characteristics of recorded voice are extracted for verification. The steps involved are 4.1.2.1 Frame Blocking: The first step of the feature extraction is to frame the speech into frames of approximately 30 msec. 4.1.2.2 Windowing: The next step in the processing is to window each individual frame so as to minimize the signal discontinuity at the beginning and end of each frame.

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4.2 Speaker Recognition using Vector Quantization VQ is a process of mapping vectors from a large vector space to a finite number of regions in that space. Each region is called a cluster and can be represented by its center called a codebook entry or centroid. A full collection of codebook entries are called a codebook. After the training process the acoustic vectors extracted from input speech of a speaker provide a set of training vectors. As described above, the next important step is to build a speaker-specific VQ codebook for this speaker using those training vectors. There is a well-know algorithm, namely the LBG algorithm , for clustering a set of L training vectors into a set of M codebook vectors. Vector quantization is a classical quantization technique from signal processing which allows the modeling of probability density functions by the distribution of prototype vectors. It was originally used for data compression. It works by dividing a large set of points (vectors) into groups having approximately the same number of points closest to them. Each group is represented by its centroid point, as in k-means and some other clustering algorithms. The density matching property of vector quantization is powerful, especially for identifying the density of large and high-dimensioned data. Since data points are represented by the index of their closest centroid, commonly occurring data have low error, and rare data high error. This is why VQ is suitable for lossy data compression. It can also be used for lossy data correction and density estimation.

In Vector Quantization you represent not individual values but (usually small) arrays of them. A typical example is a color map: a color picture can be represented by a 2D array

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of triplets (RGB values). In most pictures those triplets do not cover the whole RGB space but tend to concetrate in certain areas. For example, the picture of a forest will typically have a lot of green. One can select a relatively small subset (typically 256 elements) of representative colors, i.e RGB triplets, and then approximate each triplet by the representative of that small set. In case of 256 one can use 1 byte instead of 3 for each pixel. One can do the same for any large data sets, especialy when consecutive points are correlated in some way. CELP speech compression algorithms use those subsets "codebooks" and use them to quantize exciation vectors for linear prediction -hence the name CELP which stands for Codebook Excited Linear Prediction.

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CHAPTER 5 INTERFACE OF USER

5.1 Snap shots 5.1.1 G.U.I.:-

Figure 5.1 G.U.I.

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5.1.2 New User

Figure 5.2 New User 5.1.3 Home

Figure 5.3 Home Voice Identity Security System

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5.1.4 Already existing User

Figure 5.4 Existing User

5.1.5 Database:

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CHAPTER 6 IMPLEMENTATION AND CONCLUSION

6.1 IMPLEMENTATION This project helps in understanding the creation of voice recognition system and the technology used to implement it. The design of the project which includes interface that illustrates to the user whether the user in already existing in the database or the user is new. A precise knowledge about how MATLAB is used to develop a voice recognition system, how it accept the input voice, save the voice with different name, processing of voice & verification of voice. A good voice recognition system must be accompanied with user friendly application logic. It should be convenient for the user to input the voice & process the voice in any way he likes according to his convenient. The voice recognition system described in this project provides a number of features that are designed to make the general public more comfortable. MATLAB consist of various toolboxes. Speaker recognition has been developed with the help of Signal Processing Toolbox and Database Toolbox. Signal Processing toolbox consist of all the functions required to process an input signal. In Voice Recognition Security System the input is recorded sound. Signal Processing toolbox process the stored voices and extract features with the help of various functions available. Database toolbox is used for database operations like connection to the database, storing information in the database, fetching information from the database etc. The Database toolbox uses the DSN name of the database to access it and perform the required operations. Voice Identity Security System

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6.2 CONCLUSION 

We have successfully implemented Enrollment module.



We have developed the GUI base of the project , for the user-interaction in order to provide them a easier way to access the application.

Following are the feature that we have implemented in the project :* able to store voices in “.wav” format. * able to store username / password in database. * provide the option for the existing & new user. * Validations in each field. * implement the FFT (to convert the time domain in to frequency domain) 

Our next challenge is to implement the other modules i.e. feature-extraction & verification of the voice in order to fulfill the main motive of the project.

6.2.2 SCOPE OF IMPROVEMENTS 1. It is supposed to have sounds of all formats in the training set 2. In future, it is possible that it can be extended to recognize voice having some noise also. 3. In future, it is possible that it can recognize longer sounds. 4. Various algorithms can be implemented for Voice Recognition. 5. In future, it may be possible to integrate MATLAB and C#.NET or Java to run the modules together.

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APPENDIX Coding: Main: function varargout = main(varargin) % MAIN M-file for main.fig % MAIN, by itself, creates a new MAIN or raises the existing % singleton*. % % H = MAIN returns the handle to a new MAIN or the handle to % the existing singleton*. % % MAIN('CALLBACK',hObject,eventData,handles,...) calls the local % function named CALLBACK in MAIN.M with the given input arguments. % % MAIN('Property','Value',...) creates a new MAIN or raises the % existing singleton*. Starting from the left, property value pairs are % applied to the GUI before main_OpeningFcn gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to main_OpeningFcn via varargin. % % *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one % instance to run (singleton)". % % See also: GUIDE, GUIDATA, GUIHANDLES % Edit the above text to modify the response to help main % Last Modified by GUIDE v2.5 14-Oct-2010 00:43:02 % Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @main_OpeningFcn, ... 'gui_OutputFcn', @main_OutputFcn, ... 'gui_LayoutFcn', [] , ... 'gui_Callback', []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1}); end if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); else gui_mainfcn(gui_State, varargin{:}); end % End initialization code - DO NOT EDIT

% --- Executes just before main is made visible. function main_OpeningFcn(hObject, eventdata, handles, varargin)

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% This function has no output args, see OutputFcn. % hObject handle to figure % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % varargin command line arguments to main (see VARARGIN) % Choose default command line output for main handles.output = hObject; % Update handles structure guidata(hObject, handles); % UIWAIT makes main wait for user response (see UIRESUME) % uiwait(handles.figure1);

% --- Outputs from this function are returned to the command line. function varargout = main_OutputFcn(hObject, eventdata, handles) % varargout cell array for returning output args (see VARARGOUT); % hObject handle to figure % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Get default command line output from handles structure varargout{1} = handles.output;

% --- Executes on button press in login. function login_Callback(hObject, eventdata, handles) % hObject handle to login (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) home % --- Executes on button press in signup. function signup_Callback(hObject, eventdata, handles) % hObject handle to signup (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) login

% --- Executes during object creation, after setting all properties.

% --- Executes during object creation, after setting all properties. function axes4_CreateFcn(hObject, eventdata, handles) % hObject handle to axes4 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles empty - handles not created until after all CreateFcns called % Hint: place code in OpeningFcn to populate axes4 axes(hObject) imShow('E:\MatLab\pout.jpg')

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% --- Executes when figure1 is resized. function figure1_ResizeFcn(hObject, eventdata, handles) % hObject handle to figure1 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)

% --- Executes during object creation, after setting all properties. function axes5_CreateFcn(hObject, eventdata, handles) % hObject handle to axes5 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles empty - handles not created until after all CreateFcns called % Hint: place code in OpeningFcn to populate axes5 axes(hObject) imShow('E:\MatLab\pout.jpg')

Home: function varargout = home(varargin) % HOME M-file for home.fig % HOME, by itself, creates a new HOME or raises the existing % singleton*. % % H = HOME returns the handle to a new HOME or the handle to % the existing singleton*. % % HOME('CALLBACK',hObject,eventData,handles,...) calls the local % function named CALLBACK in HOME.M with the given input arguments. % % HOME('Property','Value',...) creates a new HOME or raises the % existing singleton*. Starting from the left, property value pairs are % applied to the GUI before home_OpeningFcn gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to home_OpeningFcn via varargin. % % *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one % instance to run (singleton)". % % See also: GUIDE, GUIDATA, GUIHANDLES % Edit the above text to modify the response to help home % Last Modified by GUIDE v2.5 14-Oct-2010 00:50:54 % Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @home_OpeningFcn, ... 'gui_OutputFcn', @home_OutputFcn, ... 'gui_LayoutFcn', [] , ... 'gui_Callback', []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1});

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end if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); else gui_mainfcn(gui_State, varargin{:}); end % End initialization code - DO NOT EDIT

% --- Executes just before home is made visible. function home_OpeningFcn(hObject, eventdata, handles, varargin) % This function has no output args, see OutputFcn. % hObject handle to figure % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % varargin command line arguments to home (see VARARGIN) % Choose default command line output for home handles.output = hObject; % Update handles structure guidata(hObject, handles); % UIWAIT makes home wait for user response (see UIRESUME) % uiwait(handles.figure1);

% --- Outputs from this function are returned to the command line. function varargout = home_OutputFcn(hObject, eventdata, handles) % varargout cell array for returning output args (see VARARGOUT); % hObject handle to figure % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Get default command line output from handles structure varargout{1} = handles.output;

function user_Callback(hObject, eventdata, handles) % hObject handle to user (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Hints: get(hObject,'String') returns contents of user as text % str2double(get(hObject,'String')) returns contents of user as a double

% --- Executes during object creation, after setting all properties. function user_CreateFcn(hObject, eventdata, handles) % hObject handle to user (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles empty - handles not created until after all CreateFcns called

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% Hint: edit controls usually have a white background on Windows. % See ISPC and COMPUTER. if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) set(hObject,'BackgroundColor','white'); end

function pass_Callback(hObject, eventdata, handles) % hObject handle to pass (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Hints: get(hObject,'String') returns contents of pass as text % str2double(get(hObject,'String')) returns contents of pass as a double

% --- Executes during object creation, after setting all properties. function pass_CreateFcn(hObject, eventdata, handles) % hObject handle to pass (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles empty - handles not created until after all CreateFcns called % Hint: edit controls usually have a white background on Windows. % See ISPC and COMPUTER. if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) set(hObject,'BackgroundColor','white'); end

% --- Executes on button press in pushbutton1. function pushbutton1_Callback(hObject, eventdata, handles) % hObject handle to pushbutton1 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) handles.str= get(handles.user,'string'); handles.str1= get(handles.pass,'string'); if isempty(handles.str) errordlg('You must enter a username value') %uicontrol(handles.insert) return end guidata(hObject, handles);

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a=(handles.str) c=(handles.str1) connA = database('db','','') ping(connA) curs = exec(connA,['select * from signup where username= ' '''' a '''']); curs=fetch(curs); b=curs.Data; curs = exec(connA,['select * from signup where password= ' '''' c '''']); curs=fetch(curs); d=curs.Data; if(strcmp(handles.str,b)) if(strcmp(handles.str1,d)) msgbox('username & password is matched') msgbox('record Your password in ...\E:\MatLab\train folder for 10 sec') open('C:\WINDOWS\system32\SoundRecorder.exe') else msgbox('invalid username & password') end else msgbox('invalid username & password') end

% --- Executes on button press in signup. function signup_Callback(hObject, eventdata, handles) % hObject handle to signup (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) login % --- Executes on button press in Exit. function Exit_Callback(hObject, eventdata, handles) % hObject handle to Exit (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) clear all; clc; close all;

Login: function varargout = login(varargin) % LOGIN M-file for login.fig % LOGIN, by itself, creates a new LOGIN or raises the existing % singleton*. %

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% H = LOGIN returns the handle to a new LOGIN or the handle to % the existing singleton*. % % LOGIN('CALLBACK',hObject,eventData,handles,...) calls the local % function named CALLBACK in LOGIN.M with the given input arguments. % % LOGIN('Property','Value',...) creates a new LOGIN or raises the % existing singleton*. Starting from the left, property value pairs are % applied to the GUI before login_OpeningFcn gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to login_OpeningFcn via varargin. % % *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one % instance to run (singleton)". % % See also: GUIDE, GUIDATA, GUIHANDLES % Edit the above text to modify the response to help login % Last Modified by GUIDE v2.5 13-Oct-2010 22:16:25 % Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @login_OpeningFcn, ... 'gui_OutputFcn', @login_OutputFcn, ... 'gui_LayoutFcn', [] , ... 'gui_Callback', []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1}); end if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); else gui_mainfcn(gui_State, varargin{:}); end % End initialization code - DO NOT EDIT

% --- Executes just before login is made visible. function login_OpeningFcn(hObject, eventdata, handles, varargin) % This function has no output args, see OutputFcn. % hObject handle to figure % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % varargin command line arguments to login (see VARARGIN) % Choose default command line output for login handles.output = hObject; % Update handles structure guidata(hObject, handles); % UIWAIT makes login wait for user response (see UIRESUME) % uiwait(handles.figure1);

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% --- Outputs from this function are returned to the command line. function varargout = login_OutputFcn(hObject, eventdata, handles) % varargout cell array for returning output args (see VARARGOUT); % hObject handle to figure % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Get default command line output from handles structure varargout{1} = handles.output;

function insert_Callback(hObject, eventdata, handles) % hObject handle to insert (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Hints: get(hObject,'String') returns contents of insert as text % str2double(get(hObject,'String')) returns contents of insert as a double

% --- Executes during object creation, after setting all properties. function insert_CreateFcn(hObject, eventdata, handles) % hObject handle to insert (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles empty - handles not created until after all CreateFcns called % Hint: edit controls usually have a white background on Windows. % See ISPC and COMPUTER. if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) set(hObject,'BackgroundColor','white'); end

% --- Executes on button press in pushbutton1. function pushbutton1_Callback(hObject, eventdata, handles) % hObject handle to pushbutton1 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) %handles.str=get(handles.insert,'String'); handles.str= get(handles.insert,'string'); handles.str1= get(handles.pass,'string'); if isempty(handles.str) errordlg('You must enter a username value') %uicontrol(handles.insert) return end %b={handles.str} guidata(hObject, handles); a=(handles.str) connA = database('db','','') ping(connA) %curs = exec(connA,....

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%

['select * from matlab where user= ' '''' handles.str ''''])

%curs = exec(connA, 'select username from signup'); %setdbprefs('DataReturnFormat','cellarray') %curs =(fetch(curs)) %a=curs.Data(:,1) %b=curs.Data curs = exec(connA,['select * from signup where username= ' '''' handles.str '''']); curs=fetch(curs); a=curs.Data; if(strcmp(handles.str,a)) msgbox('username already exist') %if(strcmp(handles.str,curs.Data)) %msgbox('username already exist'); else %if (strcmp(handles.str,curs.Data)) % errordlg('User already Exist'); %uicontrol(handles.insert) %return; %if(strcmp(handles.str,roll)) %errordlg('User already Exist'); %else %C=cell(1,1); C={handles.str,handles.str1} colnames={'username','password'} insert(connA,'signup',colnames,C) msgbox('record Your password in ...\E:\MatLab\train folder for 10 sec') open('C:\WINDOWS\system32\SoundRecorder.exe') close(curs) end

% -------------------------------------------------------------------function Untitled_1_Callback(hObject, eventdata, handles) % hObject handle to Untitled_1 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)

% -------------------------------------------------------------------function Untitled_3_Callback(hObject, eventdata, handles) % hObject handle to Untitled_3 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)

% --- Executes on key press with focus on figure1 and no controls selected.

function pass_Callback(hObject, eventdata, handles)

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% hObject handle to pass (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % Hints: get(hObject,'String') returns contents of pass as text % str2double(get(hObject,'String')) returns contents of pass as a double

% --- Executes during object creation, after setting all properties. function pass_CreateFcn(hObject, eventdata, handles) % hObject handle to pass (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles empty - handles not created until after all CreateFcns called % Hint: edit controls usually have a white background on Windows. % See ISPC and COMPUTER. if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) set(hObject,'BackgroundColor','white'); end

% --- Executes on button press in pushbutton5. function pushbutton5_Callback(hObject, eventdata, handles) % hObject handle to pushbutton5 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) home

% --- Executes on button press in pushbutton6. function pushbutton6_Callback(hObject, eventdata, handles) % hObject handle to pushbutton6 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) clear all; clc; close all;

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BIBLIOGRAPHY 1. Rabiner, L., Juang, B.H., “Fundamentals of Speech Recognition”, PrenticeHall Inc., 1993. 2. Chapman & Hall, “A Guide to MATLAB Object-Oriented Programming”, CRC, 2007 3. Knight, “Basics of MATLAB and Beyond “, CRC Press Inc., 2000. 4. “Robust Computer Voice Recognition Using Improved MFCC Algorithm” Clarence Goh Kok Leon, 2009. 5.

“Voice processing “ by Gold & Morgan.

6. www.wikipdia.com 7. www.mathworks.com

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