Comparison of discrete and continuous classifier-based HMM

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概要

• 論文の詳細を見る

• To improve discriminating ability of Hidden Markov Model(HMM), we have proposed to incorporate a classifier into HMM. In this paper, we make a comparative study of its discrete distribution version and continuous one. The classifier in discrete model discriminates the symbols that are passed to HMM, whereas the classifier in continuous model discriminates the HMM states and computes their output probabilities as classification scores. Thus, the output probability in discrete model indicates the frequency of the symbol occurrence, while that in continuous model shows the reliability of the classification for a given input. We made experimental evaluation of the both types of HMM with the same classifier, changing its output characteristics. In phoneme recognition, discrete model was superior to continuous one. In word and sentence recognition, however, we found that really stochastic distribution of the output probabilities was significant regardless of the types of HMM.

• 社団法人日本音響学会の論文

著者

• Kawahara Tatsuya

  Department Of Informationscience Kyoto University

• Kawahara Tatsuya

  Department Of Information Science Kyoto University

• Doshita Shuji

  Department Of Electronics And Informatics Faculty Of Science And Technology Ryukoku University

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