Topic tracking language model for speech recognition

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

*この研究の対応する著者

研究成果査読

27 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)440-461
ページ数22
ジャーナルComputer Speech and Language
25
2
DOI
出版ステータスPublished - 2011 4 1
外部発表はい

ASJC Scopus subject areas

  • ソフトウェア
  • 理論的コンピュータサイエンス
  • 人間とコンピュータの相互作用

フィンガープリント

「Topic tracking language model for speech recognition」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル