  Speech restoration aims to remove distortions in speech signals. Prior
methods mainly focus on a single type of distortion, such as speech denoising
or dereverberation. However, speech signals can be degraded by several
different distortions simultaneously in the real world. It is thus important to
extend speech restoration models to deal with multiple distortions. In this
paper, we introduce VoiceFixer, a unified framework for high-fidelity speech
restoration. VoiceFixer restores speech from multiple distortions (e.g., noise,
reverberation, and clipping) and can expand degraded speech (e.g., noisy
speech) with a low bandwidth to 44.1 kHz full-bandwidth high-fidelity speech.
We design VoiceFixer based on (1) an analysis stage that predicts
intermediate-level features from the degraded speech, and (2) a synthesis stage
that generates waveform using a neural vocoder. Both objective and subjective
evaluations show that VoiceFixer is effective on severely degraded speech, such
as real-world historical speech recordings. Samples of VoiceFixer are available
at https://haoheliu.github.io/voicefixer.
