voice identification and recognition system, matlab

VOICE IDENTIFICATION AND RECOGNITION SYSTEM

A SIMPLE YET COMPLEX APPROACH TO MODERN SOPHISTICATION

VOICE IDENTIFICATION AND RECOGNITION SYSTEM 1

GROUP MEMBERS

• SOHAIB TALLAT SP13-BCE-040

• FARHAN SHAHID SP13-BCE-013

• ABDUL SAMAD SP13-BCE-002

• MATTI ULLAH ABBASI SP13-BCE-025


INTRODUCTION AND INSPIRATION

• As we know that simplicity has taken its tool, it is now the age of sophisticated technologies therefore nowadays efficient security systems have to be utilised in our life.

• The “VOICE IDENTIFICATION AND RECOGNITION SYSTEM” has been developed to cater our needs for controlling access to services such as: banking, databases systems etc. which are used to secure confidential information.

• We were inspired to make this project for making lock mechanism systems speech automated, especially for the ease of physically disabled people.


ABSTRACT

• Approaches for making Voice recognition sytems:

a. Linear Prediction Coding (LPC)

b. Mel-Frequecy Cepstrum Coefficients (MFCC) and others.

• Principle Used: Mel-Frequecy Cepstrum Coefficients (MFCC)

• Working


THE VOICE IDENTIFICATION ALGORITHM

• Priciples of Speaker Recognition:

a. Identification

b. Verification

Input

speech

Feature

extraction

Reference

model

(Speaker #1)

Similarity

Reference

model

(Speaker #N)

Similarity

Maximum

selection

Identification

result

(Speaker ID)

Reference

model

(Speaker #M)

SimilarityInput

speech

Feature

extraction

Verification

result

(Accept/Reject)Decision

ThresholdSpeaker ID

(#M)


Figure 1: Speaker Identification

Figure 2: Speaker Recognition

FEATURE EXTRACTION

• Feature extraction is the process that extracts a small amount of data from the voice signal that can later be used to represent each speaker.

• A wide range of possibilities exist for parametrically representing the speech signal for the speaker recognition task, such as Mel Frequency Cepstrum Coefficients (MFCC).

0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

Time (second)


Figure 3: Example Of Speech Signal

MEL-FREQUENCY CEPSTRUM COEFFICIENTS (MFCC) PROCESSOR

mel

cepstrum

mel

spectrum

framecontinuous

speech

Frame

Blocking

Windowing FFT spectrum

Mel-frequency

WrappingCepstrum


MFCC PROCESSOR ELABORATED

• Frame Blocking

• Windowing

• Fast Fourier Transform

• Mel- Frequency Wrapping

• Cepstrum

0 1000 2000 3000 4000 5000 6000 7000 0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2 Mel-spaced filterbank

Frequency (Hz)


Figure 4: Example of mel-spaced frequency bank

FEATURE MATCHING

• Feature matching involves the actual procedure to identify the unknown speaker by comparing extracted features from his/her voice input with the ones from a set of known speakers

• The goal of pattern recognition is to classify objects of interest into one of a number of categories or classes.

• The objects of interest are called patterns and in our case are sequences of acoustic vectors that are extracted from an input speech.

• Classes are referred to individual speakers.


PATTERN RECOGNITION TECHNIQUE

• Feature matching technique used in “VOICE IDENTIFICATION AND RECOGNITION SYSTEM” is Vector Quantization (VQ).

• 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 codeword. The collection of all codewords is called a codebook.


RECOGNITION PROCESS


Speaker 1

Speaker 1centroidsample

Speaker 2centroidsample

Speaker 2

VQ distortion

Figure 5: Conceptual Diagram Illustrating Vector Quantization codebook Formation

LINDE-BUZO-GREY ALGORITHM

The Linde–Buzo–Gray algorithm (introduced by Yoseph Linde,

Andrés Buzo and Robert M. Gray in 1980) is a vector quantization

algorithm to derive a good codebook.


Findcentroid

Split eachcentroid

Clustervectors

Findcentroids

Compute D(distortion)

D

D'D

Stop

D’ = D

m = 2*m

No

Yes

Yes

Nom < M

http://en.wikipedia.org/wiki/Robert_M._Gray

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

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

THE GRAPHICAL USER INTERFACE

• There are many ways to make your own custom Graphical User Interface (GUI); you can do it manually or you can use another efficient approach that is the “Guide” approach.


Figure 6: Guide Quick Start Window

Figure 7: Our Custom GUI

EMBEDDING CODE TO THE GUI

• Note that in the figure we have six essential buttons, which perform their unique task.

a. “Add New Sound To The Database”

b. “Speaker Recognition From Mike”

c. “DATABASE INFORMATION”

d. “PLOT DATABASE”

e. “Delete Database”

f. “EXIT”


Figure 7: Our Custom GUI

ADDING BACK GROUND TO THE GUI

CODE:

% create an axes that spans the whole guiah = axes('unit', 'normalized', 'position', [0 0 1 1]); % import the background image and show it on the axesbg = imread('project image 3.jpg'); imagesc(bg); % prevent plotting over the background and turn the axis off set(ah,'handlevisibility','off','visible','off') % making sure the background is behind all the other uicontrolsuistack(ah, 'bottom');


Figure 8: Our Custom Background


Figure 9: Our Final Program

APPLICATION DEPLOYMENT


Figure 10: Standalone application deployment window Figure 11: Our Custom Splash screen

REFERENCES

• L.R. Rabiner and B.H. Juang, Fundamentals of Speech Recognition, Prentice-Hall, Englewood Cliffs, N.J., 1993.

• S.B. Davis and P. Mermelstein, “Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences”, IEEE Transactions on Acoustics, Speech, Signal Processing, Vol. ASSP-28, No. 4, August 1980

• Y. Linde, A. Buzo & R. Gray, “An algorithm for vector quantizer design”, IEEE Transactions on Communications, Vol. 28, pp.84-95, 1980

• S. Furui, “Speaker independent isolated word recognition using dynamic features of speech spectrum”, IEEE Transactions on Acoustic, Speech, Signal Processing, Vol. ASSP-34, No. 1, pp. 52-59, February 1986

• F.K. Song, A.E. Rosenberg and B.H. Juang, “A vector quantisation approach to speaker recognition”, AT&T Technical Journal, Vol. 66-2, pp. 14-26, March 1987

• comp.speech Frequently Asked Questions WWW site, http://svr-www.eng.cam.ac.uk/comp.speech/


http://svr-www.eng.cam.ac.uk/comp.speech/

voice identification and recognition system, matlab

Engineering

voice recognition sytems

recognition system8figure

recognition system5figure

recognition system6figure

speaker recognition

voice signal

recognition systema

recognition system11