  Consistency regularization has recently been applied to semi-supervised
sequence-to-sequence (S2S) automatic speech recognition (ASR). This principle
encourages an ASR model to output similar predictions for the same input speech
with different perturbations. The existing paradigm of semi-supervised S2S ASR
utilizes SpecAugment as data augmentation and requires a static teacher model
to produce pseudo transcripts for untranscribed speech. However, this paradigm
fails to take full advantage of consistency regularization. First, the masking
operations of SpecAugment may damage the linguistic contents of the speech,
thus influencing the quality of pseudo labels. Second, S2S ASR requires both
input speech and prefix tokens to make the next prediction. The static prefix
tokens made by the offline teacher model cannot match dynamic pseudo labels
during consistency training. In this work, we propose an improved consistency
training paradigm of semi-supervised S2S ASR. We utilize speech chain
reconstruction as the weak augmentation to generate high-quality pseudo labels.
Moreover, we demonstrate that dynamic pseudo transcripts produced by the
student ASR model benefit the consistency training. Experiments on LJSpeech and
LibriSpeech corpora show that compared to supervised baselines, our improved
paradigm achieves a 12.2% CER improvement in the single-speaker setting and
38.6% in the multi-speaker setting.
