  While a streaming voice assistant system has been used in many applications,
this system typically focuses on unnatural, one-shot interactions assuming
input from a single voice query without hesitation or disfluency. However, a
common conversational utterance often involves multiple queries with
turn-taking, in addition to disfluencies. These disfluencies include pausing to
think, hesitations, word lengthening, filled pauses and repeated phrases. This
makes doing speech recognition with conversational speech, including one with
multiple queries, a challenging task. To better model the conversational
interaction, it is critical to discriminate disfluencies and end of query in
order to allow the user to hold the floor for disfluencies while having the
system respond as quickly as possible when the user has finished speaking. In
this paper, we present a turntaking predictor built on top of the end-to-end
(E2E) speech recognizer. Our best system is obtained by jointly optimizing for
ASR task and detecting when the user is paused to think or finished speaking.
The proposed approach demonstrates over 97% recall rate and 85% precision rate
on predicting true turn-taking with only 100 ms latency on a test set designed
with 4 types of disfluencies inserted in conversational utterances.
