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arXiv · 2010.14318

Multitask Training with Text Data for End-to-End Speech Recognition

Peidong Wang·Tara N. Sainath·Ron J. Weiss
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Abstract

We propose a multitask training method for attention-based end-to-end speech recognition models. We regularize the decoder in a listen, attend, and spell model by multitask training it on both audio-text and text-only data. Trained on the 100-hour subset of LibriSpeech, the proposed method, without requiring an additional language model, leads to an 11% relative performance improvement over the baseline and approaches the performance of language model shallow fusion on the test-clean evaluation set. We observe a similar trend on the whole 960-hour LibriSpeech training set. Analyses of different types of errors and sample output sentences demonstrate that the proposed method can incorporate language level information, suggesting its effectiveness in real-world applications.

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Peidong Wang, Tara N. Sainath, Ron J. Weiss. 2020-10-27. Multitask Training with Text Data for End-to-End Speech Recognition. https://arxiv.org/abs/2010.14318

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