HMM continuous speech recognition using predictive LR parsing

Kenji Kita, Takeshi Kawabata, Hiroaki Saito

研究成果: Conference article査読

48 被引用数 (Scopus)

抄録

The authors propose a continuous-speech recognition method that uses an accurate and efficient parsing mechanism, an LR parser, and drives HMM (hidden Markov model) modules directly without any intervening structures such as a phoneme lattice. The method was tested in Japanese phrase recognition experiments. Two grammars were prepared, a general Japanese grammar and a task-specific grammar. The phrase recognition rate with the general grammar was 72% for top candidates and 95% for the five best candidates. With the task-specific grammar, recognition rate was 80% and 99%, respectively.

本文言語English
ページ(範囲)703-706
ページ数4
ジャーナルICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2
出版ステータスPublished - 1989 12月 1
外部発表はい
イベント1989 International Conference on Acoustics, Speech, and Signal Processing - Glasgow, Scotland
継続期間: 1989 5月 231989 5月 26

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

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