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JOIST: A Joint Speech and Text Streaming Model For ASR

2022-10-13 20:59:22
Tara N. Sainath, Rohit Prabhavalkar, Ankur Bapna, Yu Zhang, Zhouyuan Huo, Zhehuai Chen, Bo Li, Weiran Wang, Trevor Strohman
arXiv_CL
       
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.

Abstract (translated)

URL

https://arxiv.org/abs/2210.07353

PDF

https://arxiv.org/pdf/2210.07353.pdf

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