Training ASR models by Generation of Contextual Information

Kritika Singh; Dmytro Okhonko; Jun Liu; Yongqiang Wang; Frank Zhang; Ross Girshick; Sergey Edunov; Fuchun Peng; Yatharth Saraf; Geoffrey Zweig; Abdelrahman Mohamed

arXiv · 1910.12367

Training ASR models by Generation of Contextual Information

Kritika Singh·Dmytro Okhonko·Jun Liu·Yongqiang Wang·Frank Zhang·Ross Girshick·Sergey Edunov·Fuchun Peng·Yatharth Saraf·Geoffrey Zweig·Abdelrahman Mohamed

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Abstract

Supervised ASR models have reached unprecedented levels of accuracy, thanks in part to ever-increasing amounts of labelled training data. However, in many applications and locales, only moderate amounts of data are available, which has led to a surge in semi- and weakly-supervised learning research. In this paper, we conduct a large-scale study evaluating the effectiveness of weakly-supervised learning for speech recognition by using loosely related contextual information as a surrogate for ground-truth labels. For weakly supervised training, we use 50k hours of public English social media videos along with their respective titles and post text to train an encoder-decoder transformer model. Our best encoder-decoder models achieve an average of 20.8% WER reduction over a 1000 hours supervised baseline, and an average of 13.4% WER reduction when using only the weakly supervised encoder for CTC fine-tuning. Our results show that our setup for weak supervision improved both the encoder acoustic representations as well as the decoder language generation abilities.

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Kritika Singh, Dmytro Okhonko, Jun Liu, Yongqiang Wang, Frank Zhang, Ross Girshick, Sergey Edunov, Fuchun Peng, Yatharth Saraf, Geoffrey Zweig, Abdelrahman Mohamed. 2019-10-27. Training ASR models by Generation of Contextual Information. https://arxiv.org/abs/1910.12367

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