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Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition

2020-10-20 17:58:13
Yu Zhang, James Qin, Daniel S. Park, Wei Han, Chung-Cheng Chiu, Ruoming Pang, Quoc V. Le, Yonghui Wu
arXiv_SD
         
Abstract

We employ a combination of recent developments in semi-supervised learning for automatic speech recognition to obtain state-of-the-art results on LibriSpeech utilizing the unlabeled audio of the Libri-Light dataset. More precisely, we carry out noisy student training with SpecAugment using giant Conformer models pre-trained using wav2vec 2.0 pre-training. By doing so, we are able to achieve word-error-rates (WERs) 1.4%/2.6% on the LibriSpeech test/test-other sets against the current state-of-the-art WERs 1.7%/3.3%.

Abstract (translated)

URL

https://arxiv.org/abs/2010.10504

PDF

https://arxiv.org/pdf/2010.10504.pdf

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