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Proceedings Paper

Evolutionary programing for adaptive wavelets applied to speaker verification
Author(s): Chang-Gyoon Lim; Jatinder S. Bedi
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Paper Abstract

A number of parameters are extracted from speech signals using adaptive wavelets based on evolutionary programming and quasi-Newton methods. Their features will be applied to a text-independent speaker verification system. Adaptive wavelet networks are an effective tool in speech signal approximations as weighted linear combinations of translated and dilated mother wavelets. These parameters can be used as features for each speaker. Conventional quasi-Newton methods for the network have the high possibility of falling into a local minimum, at which point a self-adaptive evolutionary algorithm is applied to escape it. A set of model parameters, used as input to a fuzzy inference system, is one that has properties of low intra-speaker variation, and, at the same time, high inter-speaker variation. The fuzzy inference system proposed for speaker verification is a classifier that will determine whether the utterance is made by the authorized speaker. This fuzzy inference system derives valuable information from each model parameter of each utterance spoken by several speakers from database to construct a fuzzy rule-based verification system.

Paper Details

Date Published: 3 April 1997
PDF: 9 pages
Proc. SPIE 3078, Wavelet Applications IV, (3 April 1997); doi: 10.1117/12.271749
Show Author Affiliations
Chang-Gyoon Lim, Wayne State Univ. (United States)
Jatinder S. Bedi, Wayne State Univ. (United States)


Published in SPIE Proceedings Vol. 3078:
Wavelet Applications IV
Harold H. Szu, Editor(s)

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