Continuously Predicting and Processing Barge-in During a Live Spoken Dialogue Task

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.