Multi-head Monotonic Chunkwise Attention for Online Speech Recognition
Table 2: Character error rate (CER) on AISHELL-1. Label smoothing and SpecAugment are used for all attention-based end-to-end models. Notice that Transformer[12] is an offline model.
['model', 'CER(%) Dev', 'CER(%) Test']
We first conducted experiment on AISHELL-1. Results on this data set reported in other work are also given. The vanilla non-streaming LAS model obtains 7.51% character error rate (CER) on the development set and 9.29% CER on the test set. The performance of the online MoChA model is comparable with that of the LAS model.