@inproceedings{chi-etal-2021-align,
title = "Align-Refine: Non-Autoregressive Speech Recognition via Iterative Realignment",
author = "Chi, Ethan A. and
Salazar, Julian and
Kirchhoff, Katrin",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.154",
doi = "10.18653/v1/2021.naacl-main.154",
pages = "1920--1927",
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 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.",
}
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<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 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.</abstract>
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%0 Conference Proceedings
%T Align-Refine: Non-Autoregressive Speech Recognition via Iterative Realignment
%A Chi, Ethan A.
%A Salazar, Julian
%A Kirchhoff, Katrin
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 jun
%I Association for Computational Linguistics
%C Online
%F chi-etal-2021-align
%X 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 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.
%R 10.18653/v1/2021.naacl-main.154
%U https://aclanthology.org/2021.naacl-main.154
%U https://doi.org/10.18653/v1/2021.naacl-main.154
%P 1920-1927
Markdown (Informal)
[Align-Refine: Non-Autoregressive Speech Recognition via Iterative Realignment](https://aclanthology.org/2021.naacl-main.154) (Chi et al., NAACL 2021)
ACL