Correction of automatic speech recognition with transformer sequence-to-sequence model
Table 1: Ablative study of data augmentation techniques.
['[BOLD] Dataset', '[BOLD] Size', '[BOLD] Dev  [BOLD] clean', '[BOLD] Dev  [BOLD] other', '[BOLD] Test  [BOLD] clean', '[BOLD] Test  [BOLD] other']
In our experiments, adding sentences generated with enabled dropout and cutout was much more efficient; thus, we stick to it as our training dataset in all subsequent experiments. We also experimented with using top-k beams obtained with beam search but found that the resulting sentences lacked in diversity, often differing in a few characters only.