  The autoregressive (AR) models, such as attention-based encoder-decoder
models and RNN-Transducer, have achieved great success in speech recognition.
They predict the output sequence conditioned on the previous tokens and
acoustic encoded states, which is inefficient on GPUs. The non-autoregressive
(NAR) models can get rid of the temporal dependency between the output tokens
and predict the entire output tokens in at least one step. However, the NAR
model still faces two major problems. On the one hand, there is still a great
gap in performance between the NAR models and the advanced AR models. On the
other hand, it's difficult for most of the NAR models to train and converge. To
address these two problems, we propose a new model named the two-step
non-autoregressive transformer(TSNAT), which improves the performance and
accelerating the convergence of the NAR model by learning prior knowledge from
a parameters-sharing AR model. Furthermore, we introduce the two-stage method
into the inference process, which improves the model performance greatly. All
the experiments are conducted on a public Chinese mandarin dataset ASIEHLL-1.
The results show that the TSNAT can achieve a competitive performance with the
AR model and outperform many complicated NAR models.
