  While speech recognition Word Error Rate (WER) has reached human parity for
English, long-form dictation scenarios still suffer from segmentation and
punctuation problems resulting from irregular pausing patterns or slow
speakers. Transformer sequence tagging models are effective at capturing long
bi-directional context, which is crucial for automatic punctuation. Automatic
Speech Recognition (ASR) production systems, however, are constrained by
real-time requirements, making it hard to incorporate the right context when
making punctuation decisions. In this paper, we propose a streaming approach
for punctuation or re-punctuation of ASR output using dynamic decoding windows
and measure its impact on punctuation and segmentation accuracy across
scenarios. The new system tackles over-segmentation issues, improving
segmentation F0.5-score by 13.9%. Streaming punctuation achieves an average
BLEU-score improvement of 0.66 for the downstream task of Machine Translation
(MT).
