Semantic Segmentation with Bidirectional Language Models Improves Long-form ASR
Abstract
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.
Cite
@article{arxiv.2305.18419,
title = {Semantic Segmentation with Bidirectional Language Models Improves Long-form ASR},
author = {W. Ronny Huang and Hao Zhang and Shankar Kumar and Shuo-yiin Chang and Tara N. Sainath},
journal= {arXiv preprint arXiv:2305.18419},
year = {2023}
}
Comments
Interspeech 2023. First 3 authors contributed equally