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Text-dependent speaker recognition using wavelets and neural networks

journal contribution
posted on 2007-04-01, 00:00 authored by Chee Peng Lim, S Woo
An intelligent system for text-dependent speaker recognition is proposed in this paper. The system consists of a wavelet-based module as the feature extractor of speech signals and a neural-network-based module as the signal classifier. The Daubechies wavelet is employed to filter and compress the speech signals. The fuzzy ARTMAP (FAM) neural network is used to classify the processed signals. A series of experiments on text-dependent gender and speaker recognition are conducted to assess the effectiveness of the proposed system using a collection of vowel signals from 100 speakers. A variety of operating strategies for improving the FAM performance are examined and compared. The experimental results are analyzed and discussed.

History

Journal

Soft computing

Volume

11

Issue

6

Pagination

549 - 556

Publisher

Springer

Location

Heidelberg, Germany

ISSN

1432-7643

eISSN

1433-7479

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2006, Springer-Verlag