Effectiveness of self-supervised pre-training for speech recognition
Table 2: Comparison to previously published results in terms of WER on Librispeech. All results use 4-gram language models, except [36, 37].
['[EMPTY]', 'Labeled data', 'test clean', 'test other']
Moreover, when using the same train-clean-100 subset for fine-tuning, Discrete BERT with vq-wav2vec inputs improves by 6.5 WER (35% relative WER reduction) on test-other and 1.3 WER