Continuously predicting and processing barge-in during a live spoken dialogue task

Ethan O. Selfridge, Iker Arizmendi, Peter A. Heeman, Jason D. Williams

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Scopus citations

Abstract

Barge-in enables the user to provide input during system speech, facilitating a more natural and efficient interaction. Standard methods generally focus on singlestage barge-in detection, applying the dialogue policy irrespective of the barge-in context. Unfortunately, this approach performs poorly when used in challenging environments. We propose and evaluate a barge-in processing method that uses a prediction strategy to continuously decide whether to pause, continue, or resume the prompt. This model has greater task success and efficiency than the standard approach when evaluated in a public spoken dialogue system.

Original languageEnglish (US)
Title of host publicationSIGDIAL 2013 - 14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference
PublisherAssociation for Computational Linguistics (ACL)
Pages384-393
Number of pages10
ISBN (Electronic)9781937284954
StatePublished - 2013
Event14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2013 - Metz, France
Duration: Aug 22 2013Aug 24 2013

Publication series

NameSIGDIAL 2013 - 14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference

Other

Other14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2013
Country/TerritoryFrance
CityMetz
Period8/22/138/24/13

Keywords

  • Barge-in
  • Spoken dialogue systems

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Modeling and Simulation
  • Human-Computer Interaction

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