Self-Training for End-to-End Speech Recognition
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We revisit self-training in the context of end-to-end speech recognition. We demonstrate that training with pseudo-labels can substantially improve the accuracy of a baseline model by leveraging unlabelled data. Key to our approach are a strong baseline acoustic and language model used to generate the pseudo-labels, a robust and stable beam-search decoder, and a novel ensemble approach used to increase pseudo-label diversity. Experiments on the LibriSpeech corpus show that self-training with a single model can yield a 21 relative WER improvement on clean data over a baseline trained on 100 hours of labelled data. We also evaluate label filtering approaches to increase pseudo-label quality. With an ensemble of six models in conjunction with label filtering, self-training yields a 26 the gap between the baseline and an oracle model trained with all of the labels.
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