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arXiv · 2309.17020

Low-Resource Self-Supervised Learning with SSL-Enhanced TTS

Po-chun Hsu·Ali Elkahky·Wei-Ning Hsu·Yossi Adi·Tu Anh Nguyen·Jade Copet·Emmanuel Dupoux·Hung-yi Lee·Abdelrahman Mohamed
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Abstract

Self-supervised learning (SSL) techniques have achieved remarkable results in various speech processing tasks. Nonetheless, a significant challenge remains in reducing the reliance on vast amounts of speech data for pre-training. This paper proposes to address this challenge by leveraging synthetic speech to augment a low-resource pre-training corpus. We construct a high-quality text-to-speech (TTS) system with limited resources using SSL features and generate a large synthetic corpus for pre-training. Experimental results demonstrate that our proposed approach effectively reduces the demand for speech data by 90% with only slight performance degradation. To the best of our knowledge, this is the first work aiming to enhance low-resource self-supervised learning in speech processing.

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Po-chun Hsu, Ali Elkahky, Wei-Ning Hsu, Yossi Adi, Tu Anh Nguyen, Jade Copet, Emmanuel Dupoux, Hung-yi Lee, Abdelrahman Mohamed. 2023-09-29. Low-Resource Self-Supervised Learning with SSL-Enhanced TTS. https://arxiv.org/abs/2309.17020

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