  We present a self-supervised speech restoration method without paired speech
corpora. Because the previous general speech restoration method uses artificial
paired data created by applying various distortions to high-quality speech
corpora, it cannot sufficiently represent acoustic distortions of real data,
limiting the applicability. Our model consists of analysis, synthesis, and
channel modules that simulate the recording process of degraded speech and is
trained with real degraded speech data in a self-supervised manner. The
analysis module extracts distortionless speech features and distortion features
from degraded speech, while the synthesis module synthesizes the restored
speech waveform, and the channel module adds distortions to the speech
waveform. Our model also enables audio effect transfer, in which only acoustic
distortions are extracted from degraded speech and added to arbitrary
high-quality audio. Experimental evaluations with both simulated and real data
show that our method achieves significantly higher-quality speech restoration
than the previous supervised method, suggesting its applicability to real
degraded speech materials.
