HMM continuous speech recognition using predictive LR parsing

Kenji Kita, Takeshi Kawabata, Hiroaki Saito

Research output: Contribution to journalConference articlepeer-review

49 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)703-706
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publication statusPublished - 1989
Externally publishedYes
Event1989 International Conference on Acoustics, Speech, and Signal Processing - Glasgow, Scotland
Duration: 1989 May 231989 May 26

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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