Spike-Triggered Non-Autoregressive Transformer for End-to-End Speech Recognition
Table 2: Comparison of the effects of different trigger threshold on the inference speed. We record the time that the ST-NAT spends on decoding test set and calculate the real-time factor.
['[BOLD] Threshold  [ITALIC] β', '[BOLD] 0.1', '[BOLD] 0.3', '[BOLD] 0.5', '[BOLD] 0.7']
Explore the effects of different trigger thresholds on the inference speed. We evaluate our ST-NAT with different trigger thresholds on the inference speed. All the ST-NAT models are trained with a CTC weight of 0.6. When the trigger threshold is 0.7, the model achieves an RTF of 0.0054. It also means the model only has a latency of nearly 20 milliseconds. However, a large threshold does not mean that the model can achieve the best performance. A large trigger threshold might cause the predicted length generated by the CTC trigger to be shorter than the target length, which in turn will hurt the performance of the model. Fortunately, different trigger thresholds have only little effect on the speed of inference, which can even be ignored.