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arXiv · 2104.03416

Pushing the Limits of Non-Autoregressive Speech Recognition

Edwin G. Ng·Chung-Cheng Chiu·Yu Zhang·William Chan
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Abstract

We combine recent advancements in end-to-end speech recognition to non-autoregressive automatic speech recognition. We push the limits of non-autoregressive state-of-the-art results for multiple datasets: LibriSpeech, Fisher+Switchboard and Wall Street Journal. Key to our recipe, we leverage CTC on giant Conformer neural network architectures with SpecAugment and wav2vec2 pre-training. We achieve 1.8%/3.6% WER on LibriSpeech test/test-other sets, 5.1%/9.8% WER on Switchboard, and 3.4% on the Wall Street Journal, all without a language model.

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BibTeXRIS

Edwin G. Ng, Chung-Cheng Chiu, Yu Zhang, William Chan. 2021-04-07. Pushing the Limits of Non-Autoregressive Speech Recognition. https://doi.org/10.21437/interspeech.2021-337

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