HMM continuous speech recognition using predictive LR parsing

Kenji Kita; Takeshi Kawabata; Hiroaki Saito

HMM continuous speech recognition using predictive LR parsing

Kenji Kita, Takeshi Kawabata, Hiroaki Saito

Research output: Contribution to journal › Conference article › peer-review

Abstract

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 language	English
Pages (from-to)	703-706
Number of pages	4
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2
Publication status	Published - 1989 Dec 1
Externally published	Yes
Event	1989 International Conference on Acoustics, Speech, and Signal Processing - Glasgow, Scotland Duration: 1989 May 23 → 1989 May 26

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Cite this

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title = "HMM continuous speech recognition using predictive LR parsing",

abstract = "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.",

author = "Kenji Kita and Takeshi Kawabata and Hiroaki Saito",

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T1 - HMM continuous speech recognition using predictive LR parsing

AU - Kita, Kenji

AU - Kawabata, Takeshi

AU - Saito, Hiroaki

PY - 1989/12/1

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N2 - 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.

AB - 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.

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M3 - Conference article

AN - SCOPUS:0024934095

VL - 2

SP - 703

EP - 706

JO - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

JF - Proceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing

SN - 0736-7791

T2 - 1989 International Conference on Acoustics, Speech, and Signal Processing

Y2 - 23 May 1989 through 26 May 1989

ER -

HMM continuous speech recognition using predictive LR parsing

Abstract

ASJC Scopus subject areas

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