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

Abstract

Non-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 14× 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.