Аннотация
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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