  Error correction techniques have been used to refine the output sentences
from automatic speech recognition (ASR) models and achieve a lower word error
rate (WER) than original ASR outputs. Previous works usually use a
sequence-to-sequence model to correct an ASR output sentence autoregressively,
which causes large latency and cannot be deployed in online ASR services. A
straightforward solution to reduce latency, inspired by non-autoregressive
(NAR) neural machine translation, is to use an NAR sequence generation model
for ASR error correction, which, however, comes at the cost of significantly
increased ASR error rate. In this paper, observing distinctive error patterns
and correction operations (i.e., insertion, deletion, and substitution) in ASR,
we propose FastCorrect, a novel NAR error correction model based on edit
alignment. In training, FastCorrect aligns each source token from an ASR output
sentence to the target tokens from the corresponding ground-truth sentence
based on the edit distance between the source and target sentences, and
extracts the number of target tokens corresponding to each source token during
edition/correction, which is then used to train a length predictor and to
adjust the source tokens to match the length of the target sentence for
parallel generation. In inference, the token number predicted by the length
predictor is used to adjust the source tokens for target sequence generation.
Experiments on the public AISHELL-1 dataset and an internal industrial-scale
ASR dataset show the effectiveness of FastCorrect for ASR error correction: 1)
it speeds up the inference by 6-9 times and maintains the accuracy (8-14% WER
reduction) compared with the autoregressive correction model; and 2) it
outperforms the popular NAR models adopted in neural machine translation and
text edition by a large margin.
