  For spoken dialog systems to conduct fluid conversational interactions with
users, the systems must be sensitive to turn-taking cues produced by a user.
Models should be designed so that effective decisions can be made as to when it
is appropriate, or not, for the system to speak. Traditional end-of-turn
models, where decisions are made at utterance end-points, are limited in their
ability to model fast turn-switches and overlap. A more flexible approach is to
model turn-taking in a continuous manner using RNNs, where the system predicts
speech probability scores for discrete frames within a future window. The
continuous predictions represent generalized turn-taking behaviors observed in
the training data and can be applied to make decisions that are not just
limited to end-of-turn detection. In this paper, we investigate optimal
speech-related feature sets for making predictions at pauses and overlaps in
conversation. We find that while traditional acoustic features perform well,
part-of-speech features generally perform worse than word features. We show
that our current models outperform previously reported baselines.
