Streaming Transformer-based Acoustic Models Using Self-attention with Augmented Memory
Table 4: Experiment results on our internal video ASR tasks.
['Language', 'Model', 'clean', 'noisy']
The large network configuration, i.e. 80M-parameter models, was examined. On both languages, the proposed model consistently outperformed the LC-BLSTM baseline by 9-11% on clean test sets and 5-12% on noisy test sets. There are still some accuracy gaps compared with the transformer which has the access to the whole utterance.