Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

1.60

CiteScore

Wan-Chen Chen1,2, Ching-Tang Hsieh  2 and Chih-Hsu Hsu3

1Department of Electronic Engineering, St. John’s University, Taipei, Taiwan 251, R.O.C.
2Department of Electrical Engineering, Tamkang University, Tamsui, Taiwan 251, R.O.C.
3Department of Information Technology, Ching Kuo Institute of Management and Health, Keelung, Taiwan 203, R.O.C.


 

Received: July 8, 2007
Accepted: March 10, 2008
Publication Date: December 1, 2008

Download Citation: ||https://doi.org/10.6180/jase.2008.11.4.05  


ABSTRACT


This paper presents an effective method for speaker identification system. Based on the wavelet transform, the input speech signal is decomposed into several frequency bands, and then the linear predictive cepstral coefficients (LPCC) of each band are calculated. Furthermore, the cepstral mean normalization technique is applied to all computed features in order to provide similar parameter statistics in all acoustic environments. In order to effectively utilize these multi-band speech features, we propose a multi-band 2-stage vector quantization (VQ) as the recognition model in which different 2-stage VQ classifiers are applied independently to each band and the errors of all 2-stage VQ classifiers are combined to yield total error and a global recognition decision. Finally, the KING speech database is used to evaluate the proposed method for text-independent speaker identification. The experimental results show that the proposed method gives better performance than other recognition models proposed previously in both clean and noisy environments.


Keywords: Speaker Identification, Wavelet Transform, Linear Predictive Cepstral Coefficient (LPCC), 2-Stage Vector Quantization


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