An Empirical Study on Speech Restoration Guided by Self-supervised Speech Representation
| Author | Jaeuk Byun, Youna Ji, Soo-Whan Chung, Soyeon Choe, Min-Seok Choe |
| Publication | International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Year | 2023 |
| Link | [Paper] [arXiv] |
ABSTRACT
Enhancing speech quality is an indispensable yet difficult task as it is often complicated by a range of degradation factors. In addition to additive noise, reverberation, clipping, and speech attenuation can all adversely affect speech quality. Speech restoration aims to recover speech components from these distortions. This paper focuses on exploring the impact of self-supervised learning (SSL) on the speech restoration task. Specifically, we employ speech representation in various speech restoration networks and evaluate their performance under different distortion scenarios. Our experiments demonstrate that the contextual information provided by the SSL model can enhance speech restoration performance in various distortion scenarios, while also increasing robustness against various lengths of speech attenuation and mismatched test conditions.