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

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arxiv 2010.10504 v2 pith:ET2FI4ZP submitted 2020-10-20 eess.AS cs.LGcs.SD

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

Yu Zhang , James Qin , Daniel S. Park , Wei Han , Chung-Cheng Chiu , Ruoming Pang , Quoc V. Le , Yonghui Wu This is my paper
classification eess.AS cs.LGcs.SD
keywords automaticlearninglibrispeechrecognitionsemi-supervisedspeechstate-of-the-artwers
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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%.

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