HMM speech recognition using stochastic language models

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概要

• 論文の詳細を見る

• One of the major reasons for using language models in speech recognition is to reduce the search space. Context-free grammars or finite state grammars are suitable for this purpose. However, these models ignore the stochastic characteristics of a language. In this paper, three stochastic language models are investigated. These models are 1) a trigram model of Japanese syllables, 2) a stochastic shift/reduce model in LR parsing, and 3) a trigram model of context-free rewriting rules. These stochastic language models are incorporated into the syntax-directed HMM-based speech recognition system, and tested by phrase recognition experiments. The phrase recognition rate is improved from 88. 2% to 93. 2%.

• 社団法人日本音響学会の論文

著者

• Kita Kenji

  ATR Interpreting Telephony Research Laboratories

• Kawabata T

  Tokyo Univ. Sci. Noda‐shi Jpn

• Kawabata Takeshi

  ATR Interpreting Telephony Research Laboratories

• Hanazawa Toshiyuki

  ATR Interpreting Telephony Research Laboratories

関連論文

• LR Parsing with a Category Reachability Test Applied to Speech Recognition (Special Issue on Speech and Discourse Processing in Dialogue Systems)
• HMM speech recognition using stochastic language models
• Three Different LR Parsing Algorithms for Phoneme-Context-Dependent HMM-Based Continuous Speech Recognition (Special Issue on Speech and Discourse Processing in Dialogue Systems)

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