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

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題FUZZ 2011 - 2011 IEEE International Conference on Fuzzy Systems - Proceedings
頁面1674-1680
頁數7
DOIs
出版狀態Published - 2011
事件2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei, Taiwan
持續時間: 2011 六月 272011 六月 30

Other

Other2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011
國家/地區Taiwan
城市Taipei
期間11-06-2711-06-30

All Science Journal Classification (ASJC) codes

  • 理論電腦科學
  • 軟體
  • 人工智慧
  • 應用數學

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