High Performance Sequence-to-Sequence Model for Streaming Speech Recognition
Table 1: WER performance of the S2S model with bidirectional encoder trained with different scales of the constraint loss.
['[BOLD] Model', '[ITALIC] α', '[BOLD] SWB', '[BOLD] CH', '[BOLD] Hub5’00']
We started by using a high value for α and exponentially decreased it to train several systems for comparison. As observed during training, the constraint loss gets small quickly to a stable value depended on α. Joint training slows down the convergence of the main loss but does not have a significant impact on the final performance. Different from that, the constraint loss may largely change the behavior of the attention function. We also found an extreme case when α=0.4.