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.
@article{arxiv.2010.14318,
title = {Multitask Training with Text Data for End-to-End Speech Recognition},
author = {Peidong Wang and Tara N. Sainath and Ron J. Weiss},
journal= {arXiv preprint arXiv:2010.14318},
year = {2021}
}