Align-Refine: Non-Autoregressive Speech Recognition via Iterative Realignment

AbstractNon-autoregressive encoder-decoder models greatly improve decoding speed over autoregressive models, at the expense of generation quality. To mitigate this, iterative decoding models repeatedly infill or refine the proposal of a non-autoregressive model. However, editing at the level of output sequences limits model flexibility. We instead propose *iterative realignment*, which by refining latent alignments allows more flexible edits in fewer steps. Our model, Align-Refine, is an end-to-end Transformer which iteratively realigns connectionist temporal classification (CTC) alignments. On the WSJ dataset, Align-Refine matches an autoregressive baseline with a 14x decoding speedup; on LibriSpeech, we reach an LM-free test-other WER of 9.0% (19% relative improvement on comparable work) in three iterations. We release our code at https://github.com/amazon-research/align-refine.