Speaker Identification Using Wavelet Transform and Artificial Neural Network

This paper presents an effective method for improving the performance of speaker identification system based on schemes combines the multi resolution properly of the wavelet transform and radial basis function neural net works (RBFNN), evaluated its performance by comparing the results with other me...

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Main Author: Manal Hadi Jaber
Format: Article
Language:English
Published: Unviversity of Technology- Iraq 2011-11-01
Series:Engineering and Technology Journal
Subjects:
Online Access:https://etj.uotechnology.edu.iq/article_33248_6fc1f89f423a262819dadebe0a4b2320.pdf
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author Manal Hadi Jaber
author_facet Manal Hadi Jaber
author_sort Manal Hadi Jaber
collection DOAJ
description This paper presents an effective method for improving the performance of speaker identification system based on schemes combines the multi resolution properly of the wavelet transform and radial basis function neural net works (RBFNN), evaluated its performance by comparing the results with other method. The input speech signal is decomposed into L sub band. To capture the characteristic of the vocal tract, the liner prediction code of each (including the linear predictive code (LPC) for full band) are calculated. The radial basis function neural network (RBFNN) approach is used for matching purpose. Experimental results shows that the speaker identification using the methods achieve (combines the wavelet and RBFNN) give (100%) identification rate and higher identification rate compared with multi band liner predictive code, in this paper used Matlab program to prove the results.
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spelling doaj.art-7edaf85256994a66bceba9344f40d8a02024-02-04T17:42:44ZengUnviversity of Technology- IraqEngineering and Technology Journal1681-69002412-07582011-11-0129153242325510.30684/etj.29.15.1733248Speaker Identification Using Wavelet Transform and Artificial Neural NetworkManal Hadi JaberThis paper presents an effective method for improving the performance of speaker identification system based on schemes combines the multi resolution properly of the wavelet transform and radial basis function neural net works (RBFNN), evaluated its performance by comparing the results with other method. The input speech signal is decomposed into L sub band. To capture the characteristic of the vocal tract, the liner prediction code of each (including the linear predictive code (LPC) for full band) are calculated. The radial basis function neural network (RBFNN) approach is used for matching purpose. Experimental results shows that the speaker identification using the methods achieve (combines the wavelet and RBFNN) give (100%) identification rate and higher identification rate compared with multi band liner predictive code, in this paper used Matlab program to prove the results.https://etj.uotechnology.edu.iq/article_33248_6fc1f89f423a262819dadebe0a4b2320.pdfspeaker identificationwavelet transformlinear predictive coderadial basis function artificial neural network
spellingShingle Manal Hadi Jaber
Speaker Identification Using Wavelet Transform and Artificial Neural Network
Engineering and Technology Journal
speaker identification
wavelet transform
linear predictive code
radial basis function artificial neural network
title Speaker Identification Using Wavelet Transform and Artificial Neural Network
title_full Speaker Identification Using Wavelet Transform and Artificial Neural Network
title_fullStr Speaker Identification Using Wavelet Transform and Artificial Neural Network
title_full_unstemmed Speaker Identification Using Wavelet Transform and Artificial Neural Network
title_short Speaker Identification Using Wavelet Transform and Artificial Neural Network
title_sort speaker identification using wavelet transform and artificial neural network
topic speaker identification
wavelet transform
linear predictive code
radial basis function artificial neural network
url https://etj.uotechnology.edu.iq/article_33248_6fc1f89f423a262819dadebe0a4b2320.pdf
work_keys_str_mv AT manalhadijaber speakeridentificationusingwavelettransformandartificialneuralnetwork