  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.
