We propose a method of segmenting long-form speech by separating semantically
complete sentences within the utterance. This prevents the ASR decoder from
needlessly processing faraway context while also preventing it from missing
relevant context within the current sentence. Semantically complete sentence
boundaries are typically demarcated by punctuation in written text; but
unfortunately, spoken real-world utterances rarely contain punctuation. We
address this limitation by distilling punctuation knowledge from a
bidirectional teacher language model (LM) trained on written, punctuated text.
We compare our segmenter, which is distilled from the LM teacher, against a
segmenter distilled from a acoustic-pause-based teacher used in other works, on
a streaming ASR pipeline. The pipeline with our segmenter achieves a 3.2%
relative WER gain along with a 60 ms median end-of-segment latency reduction on
a YouTube captioning task.

通过从语言模型中提炼标点符号知识，并将其应用于分割长篇语音，我们研究出一种优于其他方法的，实现了 3.2% 相对词错误率增益以及 60ms 中位端到端延迟降低的流式自动语音识别管道分割器。