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Proceedings Paper

Bispectrum analysis for speaker identification in a noisy environment with Karhunen-Loeve transformation technique
Author(s): Benyamin Kusumoputro; Muhammad Ivan Fanany; Dian Indrawati
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Paper Abstract

The work described in this paper addresses the problem for extracting bispectrum feature of speech data. Very often the bispectrum feature extraction and data reduction are complicated due to some limiting constraints, i.e., no prior knowledge of feature's distribution and higher dimensionality of bispectrum data. In this article we developed an adaptive feature extraction mechanism based on cascade neural network in conjunction with feature's dimensionality reduction based on Karhunen-Loeve transformation technique. An adaptive codebook generation algorithm which is a cascade configuration of SOFM (Self Organizing Feature Map) and LVQ (Learning Vector Quantization) was used before the K-L transformation. The transformation was experimentally shown as an effective procedure for orthogonalization and dimensionality reduction of spectrum feature. Performance of our speaker identification system was perceived to be significantly increased even though using limited number of channels in noisy environment. We also tried to improve the capability of adaptive codebook generation algorithm by applying simplified differential competitive learning network.

Paper Details

Date Published: 17 July 2000
PDF: 7 pages
Proc. SPIE 4044, Hybrid Image and Signal Processing VII, (17 July 2000); doi: 10.1117/12.391925
Show Author Affiliations
Benyamin Kusumoputro, Univ. of Indonesia (Indonesia)
Muhammad Ivan Fanany, Univ. of Indonesia (Indonesia)
Dian Indrawati, Univ. of Indonesia (Indonesia)

Published in SPIE Proceedings Vol. 4044:
Hybrid Image and Signal Processing VII
David P. Casasent; Andrew G. Tescher, Editor(s)

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