CTC-synchronous Training for Monotonic Attention Model
Table 3: Results with SpecAugment on the TEDLIUM2
['Model Offline', 'Model Transformer [karita2019comparative]', '[ITALIC] F 30', '[ITALIC] T 40', '%WER 8.1']
We used two time masks with time mask parameter T and two frequency masks with frequency mask parameter F in [specaugment]. We applied SpecAugment to MoChA in stage-2 only because applying SpecAugment from scratch did not converge. SpecAugment did not improve the performance of the MoChA models without the guide from CTC alignments because the attention scores in Eq. CTC-ST solved this issue and led to an additional 9.0% relative improvement. Moreover, CTC-ST was robust to input mask size.