  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% of the original
labels (475h labeled data). In the result, the Noisy Student algorithm with
soft labels and consistency regularization achieves 10.4% word error rate (WER)
reduction when adding 475h of unlabeled data, corresponding to a recovery rate
of 92%. Furthermore, when iteratively adding 950h more unlabeled data, our best
SSL performance is within 5% WER increase compared to using the full labeled
training set (recovery rate: 78%).
