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.

本文介绍了使用声学线索模型来理解对话者交替的研究，提出了一种多任务神经方法，同时预测转换转变和发言者意图，实验结果表明，使用发言者意图预测的辅助任务可以提高对话中转换转变的预测性能。