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arXiv · 2210.07353

JOIST: A Joint Speech and Text Streaming Model For ASR

Tara N. Sainath·Rohit Prabhavalkar·Ankur Bapna·Yu Zhang·Zhouyuan Huo·Zhehuai Chen·Bo Li·Weiran Wang·Trevor Strohman
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Abstract

We present JOIST, an algorithm to train a streaming, cascaded, encoder end-to-end (E2E) model with both speech-text paired inputs, and text-only unpaired inputs. Unlike previous works, we explore joint training with both modalities, rather than pre-training and fine-tuning. In addition, we explore JOIST using a streaming E2E model with an order of magnitude more data, which are also novelties compared to previous works. Through a series of ablation studies, we explore different types of text modeling, including how to model the length of the text sequence and the appropriate text sub-word unit representation. We find that best text representation for JOIST improves WER across a variety of search and rare-word test sets by 4-14% relative, compared to a model not trained with text. In addition, we quantitatively show that JOIST maintains streaming capabilities, which is important for good user-level experience.

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Tara N. Sainath, Rohit Prabhavalkar, Ankur Bapna, Yu Zhang, Zhouyuan Huo, Zhehuai Chen, Bo Li, Weiran Wang, Trevor Strohman. 2022-10-13. JOIST: A Joint Speech and Text Streaming Model For ASR. https://arxiv.org/abs/2210.07353

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