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arXiv · 1805.06511

Improving End-of-turn Detection in Spoken Dialogues by Detecting Speaker Intentions as a Secondary Task

Zakaria Aldeneh·Dimitrios Dimitriadis·Emily Mower Provost
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Abstract

This work focuses on the use of acoustic cues for modeling turn-taking in dyadic spoken dialogues. Previous work has shown that speaker intentions (e.g., asking a question, uttering a backchannel, etc.) can influence turn-taking behavior and are good predictors of turn-transitions in spoken dialogues. However, speaker intentions are not readily available for use by automated systems at run-time; making it difficult to use this information to anticipate a turn-transition. To this end, we propose a multi-task neural approach for predicting turn- transitions and speaker intentions simultaneously. Our results show that adding the auxiliary task of speaker intention prediction improves the performance of turn-transition prediction in spoken dialogues, without relying on additional input features during run-time.

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Zakaria Aldeneh, Dimitrios Dimitriadis, Emily Mower Provost. 2018-05-09. Improving End-of-turn Detection in Spoken Dialogues by Detecting Speaker Intentions as a Secondary Task. https://arxiv.org/abs/1805.06511

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