  Speech restoration aims to remove distortions in speech signals. Prior
methods mainly focus on single-task speech restoration (SSR), such as speech
denoising or speech declipping. However, SSR systems only focus on one task and
do not address the general speech restoration problem. In addition, previous
SSR systems show limited performance in some speech restoration tasks such as
speech super-resolution. To overcome those limitations, we propose a general
speech restoration (GSR) task that attempts to remove multiple distortions
simultaneously. Furthermore, we propose VoiceFixer, a generative framework to
address the GSR task. VoiceFixer consists of an analysis stage and a synthesis
stage to mimic the speech analysis and comprehension of the human auditory
system. We employ a ResUNet to model the analysis stage and a neural vocoder to
model the synthesis stage. We evaluate VoiceFixer with additive noise, room
reverberation, low-resolution, and clipping distortions. Our baseline GSR model
achieves a 0.499 higher mean opinion score (MOS) than the speech enhancement
SSR model. VoiceFixer further surpasses the GSR baseline model on the MOS score
by 0.256. Moreover, we observe that VoiceFixer generalizes well to severely
degraded real speech recordings, indicating its potential in restoring old
movies and historical speeches. The source code is available at
https://github.com/haoheliu/voicefixer_main.
