  Improving the performance of end-to-end ASR models on long utterances ranging
from minutes to hours in length is an ongoing challenge in speech recognition.
A common solution is to segment the audio in advance using a separate voice
activity detector (VAD) that decides segment boundary locations based purely on
acoustic speech/non-speech information. VAD segmenters, however, may be
sub-optimal for real-world speech where, e.g., a complete sentence that should
be taken as a whole may contain hesitations in the middle ("set an alarm for...
5 o'clock").
  We propose to replace the VAD with an end-to-end ASR model capable of
predicting segment boundaries in a streaming fashion, allowing the segmentation
decision to be conditioned not only on better acoustic features but also on
semantic features from the decoded text with negligible extra computation. In
experiments on real world long-form audio (YouTube) with lengths of up to 30
minutes, we demonstrate 8.5% relative WER improvement and 250 ms reduction in
median end-of-segment latency compared to the VAD segmenter baseline on a
state-of-the-art Conformer RNN-T model.
