Spontaneous dialogue speech recognition using cross-word context constrained word graphs

Tohru Shimizu, Hirofumi Yamamoto, Hirokazu Masataki, Shoichi Matsunaga, Yoshinori Sagisaka

Research output: Contribution to journalConference article

40 Citations (Scopus)

Abstract

This paper proposes a large vocabulary spontaneous dialogue speech recognizer using cross-word context constrained word graphs. In this method, two approximation methods 'cross-word context approximation' and 'lenient language score smearing' are introduced to reduce the computational cost for word graph generation. The experimental results using a 'travel arrangement corpus' show that this recognition method achieves a word hypotheses reduction of 25-40% and a cpu-time reduction of 30-60% compared to without approximation, and that the use of class bigram scores as the expected language score for each lexicon tree node decreases the word error rate 25-30% compared to without approximation.

Original languageEnglish
Pages (from-to)145-148
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume1
Publication statusPublished - 1996 Jan 1
Externally publishedYes
EventProceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 6) - Atlanta, GA, USA
Duration: 1996 May 71996 May 10

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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