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Discriminative feature extraction application to filter bank design

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3 Author(s)
Biem, A. ; ATR Human Inf. Precessing Labs., Kyoto, Japan ; McDermott, E. ; Katagiri, S.

This paper investigates the design of a filter bank model by the discriminative feature extraction method (DFE). A filter bank-based feature extractor is optimized with the classifier's parameters for the minimization of the errors occurring at the back-end classification process. The framework of minimum classification error/generalized probabilistic descent method (MCE/GPD) is used as the basis for optimization. The method is first tested in a vowel recognition task. Analysis of the process shows how DFE extracts those parts of the spectrum that are relevant to discrimination. Then the method is applied to a multi-speaker word recognition system intended to act as telephone directory assistance operator

Published in:

Neural Networks for Signal Processing [1996] VI. Proceedings of the 1996 IEEE Signal Processing Society Workshop

Date of Conference:

4-6 Sep 1996