Share on Facebook Tweet on Twitter Share on LinkedIn Share by email
Continuously Predicting and Processing Barge-in During a Live Spoken Dialogue Task

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

Abstract

Barge-in enables the user to provide input during system speech, facilitating a more natural and efficient interaction. Standard methods generally focus on single-stage 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.

Details

Publication typeInproceedings
Published inProceedings of the SIGDIAL 2013 Conference, Metz, France
PublisherAssociation for Computational Linguistics
> Publications > Continuously Predicting and Processing Barge-in During a Live Spoken Dialogue Task