Semi-supervised ASR by End-to-end Self-training
Table 1: Performance (measured by %PER) of different methods.
['Model', '[ITALIC] dev93', '[ITALIC] eval92']
As mentioned before, we will use a base system trained only on the supervised data to kick off semi-supervised training. For this system, we set the mini-batch size to 4 and each model is trained up to 40 epochs. We apply data augmentation as described in Sec. Observe that data augmentation provides sizable gain over training on clean data only (18.52% vs. 16.83% for dev PER), leading to higher pseudo-label quality. We will always use data augmentation on supervised data from now on.