  Non-autoregressive (NAR) transformer models have achieved significantly
inference speedup but at the cost of inferior accuracy compared to
autoregressive (AR) models in automatic speech recognition (ASR). Most of the
NAR transformers take a fixed-length sequence filled with MASK tokens or a
redundant sequence copied from encoder states as decoder input, they cannot
provide efficient target-side information thus leading to accuracy degradation.
To address this problem, we propose a CTC-enhanced NAR transformer, which
generates target sequence by refining predictions of the CTC module.
Experimental results show that our method outperforms all previous NAR
counterparts and achieves 50x faster decoding speed than a strong AR baseline
with only 0.0 ~ 0.3 absolute CER degradation on Aishell-1 and Aishell-2
datasets.
