novel voice ppt

7/31/2019 Novel Voice Ppt

1/22

DEVELOPMENT OF A NOVEL VOICE

VERIFICATION SYSTEM USINGWAVELETS


2/22

AIM OF THE PROJECT

The aim of the project is to study The VoiceRecognition Using Wavelet FeatureExtraction employ wavelets in voicerecognition for studying the dynamicproperties and characteristics of the voice

signal .


3/22

INTRODUCTION

The developed voice recognition system is wordindependant voice verification system combiningthe RASTA and LPC.

The voice signal is filtered using the specialpurpose voice signal filter using the RelativeSpectral Algorithm (RASTA)

The signals are denoised and decomposed toderive the wavelet coefficients and thereby astatistical computation is carried out.


4/22

CONT....

the accuracy of the verifying sample individualvoice to his own voice is quite high (around 75%to 80%).The reliability of the signal verification isstrengthened by combining entailments fromthese two completely different aspectsof theindividual voice.


5/22

CONT....

For voice comparison purposes four out fiveindividuals are verified and the results showhigher percentage of accuracy .The accuracy of the system can be improved byincorporating advanced pattern recognitiontechniques such as Hidden Markov Model(HMM).


6/22

WAVELETS

Data

Time domain

Frequency domain


7/22

FOURIER TRANSFORMS

The Great Father Fourier - Fourier Transforms

Any Periodic function can be expressed aslinear combination of basic trigonometricfunctions

(Basis functions used are sine and cosine )

df e f X t x

dt et x f X

ift

ift

2

2

)()(

)()(


8/22

COSINE AND SINE PARTS

Real partand imaginary

part

)sin()cos(2 / )( 0 ft b ft aat x f f


9/22

DRAWBACKS

Integration from -inf to +inf

Gives frequency content of total time series but temporalinformation is lost!


10/22

SHORT TIME FOURIER TRANSFORMS

Same as usual Fourier transforms, but data is modified bymultiplication with a window functionOnly part of data at a time is taken and processed


11/22

DRAWBACKS OF STFT

Frequency and time resolutions are fixed(Wider the window width, lesser the time resolutionand more the frequency resolution and vice versa)

As frequency resolution increases, timeresolution decreases uncertainty principle

Desired: Good time resolution at highfrequencies and good frequencyresolution at low frequencies!


12/22

WHAT ARE WAVELETS?

A small waveExtends to finite interval


13/22

SOME MATHEMATICAL EXPRESSIONS

dt s

t t x

ssW )(

1),(

x ( t ) actual time series(t) wavelet function

dt t

dt t

2)(

0)(

Integrable and limited to finiteregion

Total energy finite


14/22

TYPICAL PICTURE


15/22

1D WAVELET TRANSFORM


16/22

RASTA (RELATIVE SPECTRAL ALGORITHM )

This method works by applying a band-pass filterto the energy in each frequency sub-band in orderto smooth over short-term noise variations and toremove any constant offset.stationary noises are often detected. Stationarynoises are noises that are present for the

fullperiod of a certain signal and does not havediminishing feature


17/22

CONT..

The assumption that needs to bemade is that thenoise varies slowly with respect to speech.This makes the RASTA a perfect tool to beincluded in the initial stages of voice signalfiltering to remove stationary noisesThe stationary noises that are identified are noisesin the frequency range of 1Hz - 100Hz .


18/22

SYSTEM IMPLEMENTATION


19/22

RESULTS


20/22

CONT...


21/22

CONCLUSION

The Voice Recognition Using Wavelet FeatureExtraction employ wavelets in voice recognition forstudying the dynamic properties and characteristicsofthe voice signal. The voice recognition system that is

developed is word dependant voice verification systemused to verify the identity of an individual based ontheir own voice signal using the statistical computation,formant estimation and wavelet energy.. By using thefifty preloaded voice signals from five individuals, theverification tests have been carried and an accuracy rateof approximately 80 % has been achieved


22/22

THANK YOU

novel voice ppt

Documents