DEVELOPMENT OF A NOVEL VOICE VERIFICATION SYSTEM
USING WAVELETS
SYNOPSIS
ABSTRACT
The audio signal especially voice signal is becoming one of the major part in human’s
daily lives The fundamental idea of this project is to use wavelets as a mean of extracting
features from a voice signal. Current methods used in the field of signal processing include
Fourier Transform and Short Term Fourier Transform (STFT). However due to severe
limitations imposed by both the Fourier Transform and Short Term Fourier Transform in
analyzing signals deems them ineffective in analyzing complex and dynamic signals such as the
voice signal. In order to substitute the shortcomings imposed by both the common signal
processing methods, the wavelet signal processing technique is used. The wavelet technique is
used to extract the features in the voice signal by processing data at different scales. The wavelet
technique manipulates the scales to give a higher correlation in detecting the various frequency
components in the signal. Extracting the features of the voice signal does not limit the
capabilities of this technique to a particular application alone, but it opens the door to a wide
range of possibilities as different applications can benefit from the voice extracted features.
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Introduction
Wavelets are mathematical functions that satisfy certain requirements. From a
mathematical point of view, the wavelet is described as a function that should integrate to zero
and it has a waveform that has a limited duration. The wavelet is also finite of length which
means that it is compactly supported. Wavelet analyzes a signal using different scales. This
approach towards signal processing is called multi-resolution analysis. In order to implement the
system, a certain methodology is implemented by decomposing the voice signal to its
approximation and detail. From the approximation and detail coefficients that are extracted, the
methodology is implemented in order to carry out the recognition process.
The proposed methodology for the recognition phase is the statistical calculation. Four
different types of statistical calculations are carried out on the coefficients. The statistical
calculations that are carried out are mean, standard deviation, variance and mean of absolute
deviation. The wavelet that is used for the system is the symlet 7 wavelet as that this wavelet has
a very close correlation with the voice signal. This is determined through numerous trial and
errors.
The coefficients that are extracted from the wavelet decomposition process is the second
level coefficients as the level two coefficients contain most of the correlated data of the voice
signal. The data at higher levels contains very little amount of data deeming it unusable for the
recognition phase.
However a wavelet transform and a traditional transform such as IHS
transform or a PCA transform are integrated for better fusion results may be achieved .This paper
presents a novel voice verification system using wavelet transforms. The conventional signal
processing techniques assume the signal to be stationary and are ineffective in recognizing non
stationary signals such as the voice signals. Voice signals which are more dynamic could be
analyzed with far better accuracy using wavelet transform. The developed voice recognition
system is word dependant voice verification system combining the RASTA and LPC. The voice
signal is filtered using the special purpose voice signal filter using the Relative Spectral
Algorithm (RASTA). The signals are de-noised and decomposed to derive the wavelet
Coefficients and thereby a statistical computation is carried out.
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Further the formant or the resonance of the voices signal is detected using the Linear
Predictive Coding (LPC). With the statistical computation on the coefficients alone, the accuracy
of the verifying sample individual voice to his own voice is quite high (around 75% to 80%).
BLOCK DIAGRAM
Figure Complete System Flowchart
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APPLICATIONS

Speech recognition system,

Speech to text translators,

Voice based security system.

The Voice Recognition Using Wavelet Feature Extraction employs wavelets in voice
recognition for studying the dynamic properties and characteristics of the voice signal.

This is carried out by estimating the formant and detecting the pitch of the vice signal by
using LPC (Linear Predictive Coding). The voice recognition system that is developed is
word
REFERENCES
[1] Soon torn Braintree, Ying-Jui Chen Et.al. IEEE Transactions on Signal Processing, IFFT,
Vol. 50, No. 3, March 2002
[2] Kelly Wong, Journal of Undergraduate Research, the Role of the Fourier Transform in TimeScale Modification, University of Florida, Vol 2, Issue 11 - August 2001
[3] Bao Liu, Sherman Riemenschneider, an Adaptive Time- Frequency Representation and Its
Fast Implementation, Department of Mathematics, West Virginia University
Head office: 2nd floor, Solitaire plaza, beside Image Hospital, Ameerpet, Hyderabad
www.kresttechnology.com, E-Mail : krestinfo@gmail.com , Ph: 9885112363 / 040 44433434
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