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

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

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

11 Citations (Scopus)

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

Other

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

Fingerprint

Barges
Spoken Dialogue Systems
Processing
Continue
Evaluate
Prediction
Interaction
Dialogue
Standards
Model
Strategy
Policy
Context
Speech

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

Cite this

Selfridge, E. O., Arizmendi, I., Heeman, P., & Williams, J. D. (2013). Continuously predicting and processing barge-in during a live spoken dialogue task. In SIGDIAL 2013 - 14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference (pp. 384-393). Association for Computational Linguistics (ACL).

Continuously predicting and processing barge-in during a live spoken dialogue task. / Selfridge, Ethan O.; Arizmendi, Iker; Heeman, Peter; Williams, Jason D.

SIGDIAL 2013 - 14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2013. p. 384-393.

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

Selfridge, EO, Arizmendi, I, Heeman, P & Williams, JD 2013, Continuously predicting and processing barge-in during a live spoken dialogue task. in SIGDIAL 2013 - 14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference. Association for Computational Linguistics (ACL), pp. 384-393, 14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2013, Metz, France, 8/22/13.
Selfridge EO, Arizmendi I, Heeman P, Williams JD. Continuously predicting and processing barge-in during a live spoken dialogue task. In SIGDIAL 2013 - 14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference. Association for Computational Linguistics (ACL). 2013. p. 384-393
Selfridge, Ethan O. ; Arizmendi, Iker ; Heeman, Peter ; Williams, Jason D. / Continuously predicting and processing barge-in during a live spoken dialogue task. SIGDIAL 2013 - 14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2013. pp. 384-393
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