Spike-Triggered Non-Autoregressive Transformer for End-to-End Speech Recognition
Table 1: Comparison of the model with different CTC weights α and trigger thresholds β. We evaluate the CER(%) on development and test set, respectively.
['[BOLD] CTC  [BOLD] Weight  [ITALIC] α', '[BOLD] Trigger Threshold  [ITALIC] β 0.1', '[BOLD] Trigger Threshold  [ITALIC] β 0.3', '[BOLD] Trigger Threshold  [ITALIC] β 0.5', '[BOLD] Trigger Threshold  [ITALIC] β 0.7']
We train the ST-NAT model with different CTC weights α and trigger thresholds β from scratch. At the same threshold, the trigger NAT with weight 0.6 can achieve the best performance on development set. The CTC weights and trigger thresholds affect the performance of the model in different aspects. The CTC weights α are used to balance the performance of CTC trigger module and decoder. However, the trigger threshold β are used to determine how many encoder states are triggered. Both weights and thresholds play important roles in the performance of the models.