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 accuracy of popular NAR models adopted in neural machine translation by a large margin.
翻译:使用错误校正技术来完善自动语音识别( ASR) 模型的输出句, 并实现比原始 ASR 输出值低的字错误率( WER) 。 先前的作品通常使用一个序列到序列的错误率模型来自动纠正 ASR 输出句子, 造成大延迟, 无法在在线 ASR 服务中应用。 一个直接的解决方案, 由非偏向性( NAR) 神经机翻译所启发, 用于为 ASR 校正错误校正使用一个 NAR 序列生成模型模型生成模型生成模型生成模型生成模型, 但是, 其成本是大大提高 ASR 的误差率率。 在本文中, 观察明显错误率和修正操作( 即插入、删除和替换), 我们提议FastCorrect, 一个新的 NAR 错误校正校正模型校正模型校正每个源的源代码, 在版本/ IMLI 中, A 将A 和 massill 的箭头序列中, 将A- mill 的箭头值转换为A- trueal 。