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We present a self-supervised speech restoration method without paired speechcorpora. Because the previous general speech restoration method uses artificialpaired data created by applying various distortions to high-quality speechcorpora, it cannot sufficiently represent acoustic distortions of real data,limiting the applicability. Our model consists of analysis, synthesis, andchannel modules that simulate the recording process of degraded speech and istrained with real degraded speech data in a self-supervised manner. Theanalysis module extracts distortionless speech features and distortion featuresfrom degraded speech, while the synthesis module synthesizes the restoredspeech waveform, and the channel module adds distortions to the speechwaveform. Our model also enables audio effect transfer, in which only acousticdistortions are extracted from degraded speech and added to arbitraryhigh-quality audio. Experimental evaluations with both simulated and real datashow that our method achieves significantly higher-quality speech restorationthan the previous supervised method, suggesting its applicability to realdegraded speech materials.

SelfRemaster: Self-Supervised Speech Restoration with Analysis-by-Synthesis Approach Using Channel Modeling