  Attention-based encoder-decoder (AED) models have achieved promising
performance in speech recognition. However, because the decoder predicts text
tokens (such as characters or words) in an autoregressive manner, it is
difficult for an AED model to predict all tokens in parallel. This makes the
inference speed relatively slow. We believe that because the encoder already
captures the whole speech utterance, which has the token-level relationship
implicitly, we can predict a token without explicitly autoregressive language
modeling. When the prediction of a token does not rely on other tokens, the
parallel prediction of all tokens in the sequence is realizable. Based on this
idea, we propose a non-autoregressive speech recognition model called LASO
(Listen Attentively, and Spell Once). The model consists of an encoder, a
decoder, and a position dependent summarizer (PDS). The three modules are based
on basic attention blocks. The encoder extracts high-level representations from
the speech. The PDS uses positional encodings corresponding to tokens to
convert the acoustic representations into token-level representations. The
decoder further captures token-level relationships with the self-attention
mechanism. At last, the probability distribution on the vocabulary is computed
for each token position. Therefore, speech recognition is re-formulated as a
position-wise classification problem. Further, we propose a cross-modal
transfer learning method to refine semantics from a large-scale pre-trained
language model BERT for improving the performance.
