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arXiv · 2206.14716

Improving Deliberation by Text-Only and Semi-Supervised Training

Ke Hu·Tara N. Sainath·Yanzhang He·Rohit Prabhavalkar·Trevor Strohman·Sepand Mavandadi·Weiran Wang
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Abstract

Text-only and semi-supervised training based on audio-only data has gained popularity recently due to the wide availability of unlabeled text and speech data. In this work, we propose incorporating text-only and semi-supervised training into an attention-based deliberation model. By incorporating text-only data in training a bidirectional encoder representation from transformer (BERT) for the deliberation text encoder, and large-scale text-to-speech and audio-only utterances using joint acoustic and text decoder (JATD) and semi-supervised training, we achieved 4%-12% WER reduction for various tasks compared to the baseline deliberation. Compared to a state-of-the-art language model (LM) rescoring method, the deliberation model reduces the Google Voice Search WER by 11% relative. We show that the deliberation model also achieves a positive human side-by-side evaluation compared to the state-of-the-art LM rescorer with reasonable endpointer latencies.

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Ke Hu, Tara N. Sainath, Yanzhang He, Rohit Prabhavalkar, Trevor Strohman, Sepand Mavandadi, Weiran Wang. 2022-06-29. Improving Deliberation by Text-Only and Semi-Supervised Training. https://arxiv.org/abs/2206.14716

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