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Development of a speaker recognition system using wavelets and artificial neural networks

conference contribution
posted on 2001-01-01, 00:00 authored by S Woo, Chee Peng LimChee Peng Lim, R Osman
This paper addresses the problem of speaker recognition from speech signals. The study focuses on the development of a speaker recognition system comprising two modules: a wavelet-based feature extractor, and a neural-network-based classifier. We have conducted a number of experiments to investigate the applicability of Discrete Wavelet Transform (D WT) in extracting discriminative features from the speech signals, and have examined various models from the Adaptive Resonance Theory (ART) family of neural networks in classijjing the extracted features. The results indicate that DWT could be a potential feature extraction tool for speaker recognition. In addition, the ART-based classijiers have yielded very promising recognition accuracy at more than 81%.

History

Event

International Symposium on Intelligent Multimedia, Video and Speech Processing (1st : 2001 : Hong Kong, China)

Pagination

413 - 416

Publisher

IEEE

Location

Hong Kong

Place of publication

Piscataway, N. J.

Start date

2001-05-02

End date

2001-05-04

Language

eng

Publication classification

E1.1 Full written paper - refereed

Title of proceedings

ISIMP 2001 : Proceedings of the 2001 IEEE International Symposium on Intelligent Multimedia, Video and Speech Processing