Back to Search Start Over

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

Authors :
Hsu, Po-chun
Elkahky, Ali
Hsu, Wei-Ning
Adi, Yossi
Nguyen, Tu Anh
Copet, Jade
Dupoux, Emmanuel
Lee, Hung-yi
Mohamed, Abdelrahman
Publication Year :
2023

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.<br />Comment: ASRU 2023 SPARKS Workshop

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2309.17020
Document Type :
Working Paper