SpecAugment: A Simple Data Augmentation Methodfor Automatic Speech Recognition
Table 3: LibriSpeech 960h WERs (%).
['[BOLD] Method', '[BOLD] No LM  [BOLD] clean', '[BOLD] No LM  [BOLD] other', '[BOLD] With LM  [BOLD] clean', '[BOLD] With LM  [BOLD] other']
We take the largest network, LAS-6-1280, and use schedule L (with training time ∼ 24 days) and policy LD to train the network to maximize performance. We turn label smoothing on for time steps <140k as noted before. The test set performance is reported by evaluating the checkpoint with best dev-other performance. State of the art performance is achieved by the LAS-6-1280 model, even without a language model. We can incorporate an LM using shallow fusion to further improve performance.