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Apply fuzzy vector quantization to improve the observation-based Discrete Hidden Markov Model — An example on electroencephalogram (EEG) signal recognition

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4 Author(s)
Shing-Tai Pan ; Dept. of Comput. Sci. & Inf. Eng., Nat. Univ. of Kaohsiung, Kaohsiung, Taiwan ; Sheng-Fu Liang ; Tzung-Pei Hong ; Jian-Hong Zeng

This paper applies fuzzy vector quantization (FVQ) to the modeling of observation-based Discrete Hidden Markov Model (DHMM) and then to improve the speech recognition rate for the Mandarin speech. Vector quantization based on a codebook is a fundamental process to recognize the speech signal by DHMM. A codebook will be first trained by K means algorithms using Mandarin training speech. Then, based on the trained codebook, the speech features are quantized by the fuzzy sets defined on each vectors of the codebook. Subsequently, the quantized speech features are statistically applied to train the model of DHMM for the speech recognition. All the speech features to be recognized should go through the FVQ based on the fuzzy codebook before being fed into the DHMM model for recognition. Experimental results in this paper shows that the speech recognition rate can be improved by using FVQ algorithm to train the model of DHMM.

Published in:

Fuzzy Systems (FUZZ), 2011 IEEE International Conference on

Date of Conference:

27-30 June 2011

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