Topic tracking language model for speech recognition

Shinji Watanabe, Tomoharu Iwata, Takaaki Hori, Atsushi Sako, Yasuo Ariki

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

In a real environment, acoustic and language features often vary depending on the speakers, speaking styles and topic changes. To accommodate these changes, speech recognition approaches that include the incremental tracking of changing environments have attracted attention. This paper proposes a topic tracking language model that can adaptively track changes in topics based on current text information and previously estimated topic models in an on-line manner. The proposed model is applied to language model adaptation in speech recognition. We use the MIT OpenCourseWare corpus and Corpus of Spontaneous Japanese in speech recognition experiments, and show the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)440-461
Number of pages22
JournalComputer Speech and Language
Volume25
Issue number2
DOIs
Publication statusPublished - 2011 Apr 1
Externally publishedYes

Keywords

  • Language model
  • Latent topic model
  • On-line algorithm
  • Speech recognition
  • Topic tracking

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Human-Computer Interaction

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