Semi-Supervised Learning with Data Augmentation for End-to-End ASR
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In this paper, we apply Semi-Supervised Learning (SSL) along with Data Augmentation (DA) for improving the accuracy of End-to-End ASR. We focus on the consistency regularization principle, which has been successfully applied to image classification tasks, and present sequence-to-sequence (seq2seq) versions of the FixMatch and Noisy Student algorithms. Specifically, we generate the pseudo labels for the unlabeled data on-the-fly with a seq2seq model after perturbing the input features with DA. We also propose soft label variants of both algorithms to cope with pseudo label errors, showing further performance improvements. We conduct SSL experiments on a conversational speech data set with 1.9kh manually transcribed training data, using only 25 labels (475h labeled data). In the result, the Noisy Student algorithm with soft labels and consistency regularization achieves 10.4 reduction when adding 475h of unlabeled data, corresponding to a recovery rate of 92 SSL performance is within 5 training set (recovery rate: 78
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