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

Shing Tai Pan, Sheng-Fu Liang, Tzung Pei Hong, Jian Hong Zeng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationFUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings
Pages1674-1680
Number of pages7
DOIs
Publication statusPublished - 2011
Event2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei, Taiwan
Duration: 2011 Jun 272011 Jun 30

Other

Other2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011
CountryTaiwan
CityTaipei
Period11-06-2711-06-30

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Software
  • Artificial Intelligence
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Apply fuzzy vector quantization to improve the observation-based Discrete Hidden Markov Model An example on electroencephalogram (EEG) signal recognition'. Together they form a unique fingerprint.

Cite this