  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.
