@inproceedings{92338b98a0f84864845b4e89041945b9,
title = "Continuously predicting and processing barge-in during a live spoken dialogue task",
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.",
keywords = "Barge-in, Spoken dialogue systems",
author = "Selfridge, {Ethan O.} and Iker Arizmendi and Heeman, {Peter A.} and Williams, {Jason D.}",
note = "Publisher Copyright: {\textcopyright} 2013 Association for Computational Linguistics.; 14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, SIGDIAL 2013 ; Conference date: 22-08-2013 Through 24-08-2013",
year = "2013",
language = "English (US)",
series = "SIGDIAL 2013 - 14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "384--393",
booktitle = "SIGDIAL 2013 - 14th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference",
}