  Although attention based end-to-end models have achieved promising
performance in speech recognition, the multi-pass forward computation in
beam-search increases inference time cost, which limits their practical
applications. To address this issue, we propose a non-autoregressive end-to-end
speech recognition system called LASO (listen attentively, and spell once).
Because of the non-autoregressive property, LASO predicts a textual token in
the sequence without the dependence on other tokens. Without beam-search, the
one-pass propagation much reduces inference time cost of LASO. And because the
model is based on the attention based feedforward structure, the computation
can be implemented in parallel efficiently. We conduct experiments on publicly
available Chinese dataset AISHELL-1. LASO achieves a character error rate of
6.4%, which outperforms the state-of-the-art autoregressive transformer model
(6.7%). The average inference latency is 21 ms, which is 1/50 of the
autoregressive transformer model.
