Speech Classification Based on Fuzzy Adaptive Resonance Theory

DOI

10.2991/jcis.2006.297How to use a DOI?

Keywords

speech classification, fuzzy, ART

Abstract

This paper presents a neuro-fuzzy system to speech classification. We propose a multi-resolution feature extraction technique to deal with adaptive frame size. We utilize fuzzy adaptive resonance theory (FART) to cluster each frame. FART was an extension to ART, performs clustering of its inputs via unsupervised learning. ART describes a family of self-organizing neural networks, capable of clustering arbitrary sequences of input patterns into stable recognition codes. In our experiments, the TIMIT database is used and extracts features of each phoneme. The performance of speech classification is 88.66%, demonstrate the effectiveness of the proposed system is encouraging.

Copyright

© 2006, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chih-Hsu Hsu
AU  - Ching-Tang Hsieh
PY  - 2006/10
DA  - 2006/10
TI  - Speech Classification Based on Fuzzy Adaptive Resonance Theory
BT  - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.297
DO  - 10.2991/jcis.2006.297
ID  - Hsu2006/10
ER  -