Predicting ASR errors by exploiting barge-in rate of individual users for spoken dialogue systems

Kazunori Komatani*, Tatsuya Kawahara, Hiroshi G. Okuno

*この研究の対応する著者

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

We exploit the barge-in rate of individual users to predict automatic speech recognition (ASR) errors. A barge-in is a situation in which a user starts speaking during a system prompt, and it can be detected even when ASR results are not reliable. Such features not using ASR results can be a clue for managing a situation in which user utterances cannot be successfully recognized. Since individual users in our system can be identified by their phone numbers, we accumulate how often each user barges in and use this rate as a user profile for determining whether a current "barge-in" utterance should be accepted or not. We furthermore set a window that reflects the temporal transition of the user's behavior as they get accustomed to the system. Experimental results show that setting the window improves the prediction accuracy of whether the utterance should be accepted or not. The experiments also clarify the minimum window width for improving accuracy.

本文言語English
ホスト出版物のタイトルProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
ページ183-186
ページ数4
出版ステータスPublished - 2008
外部発表はい
イベントINTERSPEECH 2008 - 9th Annual Conference of the International Speech Communication Association - Brisbane, QLD, Australia
継続期間: 2008 9月 222008 9月 26

Other

OtherINTERSPEECH 2008 - 9th Annual Conference of the International Speech Communication Association
国/地域Australia
CityBrisbane, QLD
Period08/9/2208/9/26

ASJC Scopus subject areas

  • 人間とコンピュータの相互作用
  • 信号処理
  • ソフトウェア
  • 感覚系

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