Managing out-of-grammar utterances by topic estimation with domain extensibility in multi-domain spoken dialogue systems

Kazunori Komatani*, Satoshi Ikeda, Tetsuya Ogata, Hiroshi G. Okuno

*この研究の対応する著者

研究成果: Article査読

6 被引用数 (Scopus)

抄録

Spoken dialogue systems must inevitably deal with out-of-grammar utterances. We address this problem in multi-domain spoken dialogue systems, which deal with more tasks than a single-domain system. We defined a topic by augmenting a domain about which users want to find more information, and we developed a method of recovering out-of-grammar utterances based on topic estimation, i.e., by providing a help message in the estimated domain. Moreover, domain extensibility, that is, the ability to add new domains to the system, should be inherently retained in multi-domain systems. To estimate domains without sacrificing extensibility, we collected documents from the Web as training data. Since the data contained a certain amount of noise, we used latent semantic mapping (LSM), which enables robust topic estimation by removing the effects of noise from the data. Experimental results showed that our method improved topic estimation accuracy by 23.2 points for data including out-of-grammar utterances.

本文言語English
ページ(範囲)863-870
ページ数8
ジャーナルSpeech Communication
50
10
DOI
出版ステータスPublished - 2008 10
外部発表はい

ASJC Scopus subject areas

  • ソフトウェア
  • モデリングとシミュレーション
  • 通信
  • 言語および言語学
  • 言語学および言語
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ サイエンスの応用

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