# An empirical study on speech restoration guided by self supervised speech representation

arXiv: https://arxiv.org/abs/2305.18739 · HTML: https://mlsanity.com/paper/2305.18739/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2023.

## Built on by (most cited first, 2 of 2)

- [FINALLY: fast and universal speech enhancement with studio-like quality](https://mlsanity.com/paper/2410.05920/) (2024)
- [A Dual-Branch Parallel Network for Speech Enhancement and Restoration](https://mlsanity.com/paper/2409.08702/) (2024)

## Abstract (arXiv)

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 speech representation learning on the speech restoration task. Specifically, we employ speech representation in various speech restoration networks and evaluate their performance under complicated distortion scenarios. Our experiments demonstrate that the contextual information provided by the self-supervised speech representation can enhance speech restoration performance in various distortion scenarios, while also increasing robustness against the duration of speech attenuation and mismatched test conditions.