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
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

@article{2fed78211aed4304a005d8873872ca61,

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",

year = "1989",

language = "English",

volume = "2",

pages = "703--706",

journal = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

issn = "0736-7791",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "1989 International Conference on Acoustics, Speech, and Signal Processing ; Conference date: 23-05-1989 Through 26-05-1989",

}

TY - JOUR

T1 - HMM continuous speech recognition using predictive LR parsing

AU - Kita, Kenji

AU - Kawabata, Takeshi

AU - Saito, Hiroaki

PY - 1989

Y1 - 1989

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.

UR - https://www.scopus.com/pages/publications/0024934095

UR - https://www.scopus.com/pages/publications/0024934095#tab=citedBy

M3 - Conference article

AN - SCOPUS:0024934095

SN - 0736-7791

VL - 2

SP - 703

EP - 706

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

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

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

Other files and links

Fingerprint

Cite this