Enhancing Monotonic Multihead Attention for Streaming ASR
Table 3: Results of streaming MMA models on the Librispeech, TEDLIUM2, and AISHELL-1 test sets
['Model', 'Model', '%WER Librispeech', '%WER Librispeech', '%WER TED LIUM2', '%CER AISH ELL-1']
We also included results on TEDLIUM2 [tedlium2] and AISHELL-1 [aishell] to check whether the optimal configuration tuned on Librispeech can work in other corpora as well. We adopted the chunk hopping mechanism [dong2019self] for the online encoder. Following [cif, miao2020transformer], we set the left/current (hop)/right chunk sizes to 960/640/320 (narrow chunk) and 640/1280/640 (wide chunk) [ms]. We used speed perturbation [speed_perturbation] and SpecAugment [specaugment] for data augmentation, but speed perturbation was applied by default for TEDLIUM2 and AISHELL-1. For large models, we used (dmodel, dff, H) = (768, 3072, 8) and other hyperparameters were kept. Head-synchronous decoding was used for all MMA models. We used CTC scores during inference only for standard Transformer models. Our streaming MMA models achieved comparable results to the previous works and standard Transformer models without looking back to the first input frame. Especially, our model outperformed the MMA model with w=∞ [tsunoo2019towards] by a large margin. Increasing the model size was also effective. The streamabilities of the streaming MMA models on TEDLIUM2 and AISHELL-1 were 80.0%, and 81.5%, respectively. This confirms that the E5 setting generalizes to other corpora without architecture modification.