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

10/20/2020 ∙ by Yu Zhang, et al. ∙ 0

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 test/test-other sets against the current state-of-the-art WERs 1.7

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