  Non-autoregressive transformer models have achieved extremely fast inference
speed and comparable performance with autoregressive sequence-to-sequence
models in neural machine translation. Most of the non-autoregressive
transformers decode the target sequence from a predefined-length mask sequence.
If the predefined length is too long, it will cause a lot of redundant
calculations. If the predefined length is shorter than the length of the target
sequence, it will hurt the performance of the model. To address this problem
and improve the inference speed, we propose a spike-triggered
non-autoregressive transformer model for end-to-end speech recognition, which
introduces a CTC module to predict the length of the target sequence and
accelerate the convergence. All the experiments are conducted on a public
Chinese mandarin dataset AISHELL-1. The results show that the proposed model
can accurately predict the length of the target sequence and achieve a
competitive performance with the advanced transformers. What's more, the model
even achieves a real-time factor of 0.0056, which exceeds all mainstream speech
recognition models.
