A stochastic speech understanding method to generate interlingual representations

Koichi Tanigaki*, Yoshinori Sagisaka

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The purpose of this paper is to create an understanding system that can reduce the recognition error that prevents semantic analysis, and that is robust to speech input. A method is proposed in which semantic analysis and speech recognition are integrated on the basis of a statistical model, and the procedure is optimized as a high-level recognition process from speech to semantics. As a method of realizing the idea effectively and efficiently, search of the word graph by means of a decision tree is proposed. Using the proposed method, an identification experiment is performed for the semantics represented by the intermediate language. It is a problem of segmentation of utterances into an average of 1.6 semantic units, and the assignment of the semantic units to 7600 classes characterized by combinations of utterance action and concept. In the speech recognition experiment the understanding error is improved by 8.5% compared to the case in which the 1-best recognition candidate is input.

Original languageEnglish
Pages (from-to)52-62
Number of pages11
JournalSystems and Computers in Japan
Issue number3
Publication statusPublished - 2002 Mar
Externally publishedYes


  • Decision tree
  • Intermediate language
  • Speech understanding
  • Word graph

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture
  • Computational Theory and Mathematics


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