Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages

• URL: http://arxiv.org/abs/2205.01086v1
• Date: Mon, 2 May 2022 17:59:02 GMT
• Title: Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages
• Authors: Felix Wu, Kwangyoun Kim, Shinji Watanabe, Kyu Han, Ryan McDonald, Kilian Q. Weinberger, Yoav Artzi
• Abstract summary: We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data. We induce a pseudo language as a compact discrete representation, and formulate a self-supervised pseudo speech recognition task. This process stands on its own, or can be applied as low-cost second-stage pre-training.
• Score: 58.43299730989809
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data. We induce a pseudo language as a compact discrete representation, and formulate a self-supervised pseudo speech recognition task -- transcribing audio inputs into pseudo subword sequences. This process stands on its own, or can be applied as low-cost second-stage pre-training. We experiment with automatic speech recognition (ASR), spoken named entity recognition, and speech-to-text translation. We set new state-of-the-art results for end-to-end spoken named entity recognition, and show consistent improvements on 20 language pairs for speech-to-text translation, even when competing methods use additional text data for training. Finally, on ASR, our approach enables encoder-decoder methods to benefit from pre-training for all parts of the network, and shows comparable performance to highly optimized recent methods.

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