Pushing the Limits of Non-Autoregressive Speech Recognition

• URL: http://arxiv.org/abs/2104.03416v1
• Date: Wed, 7 Apr 2021 22:17:20 GMT
• Title: Pushing the Limits of Non-Autoregressive Speech Recognition
• Authors: Edwin G. Ng, Chung-Cheng Chiu, Yu Zhang, William Chan
• Abstract summary: We push the limits of non-autoregressive state-of-the-art results for multiple datasets. 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.
• Score: 24.299771352483322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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