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Human subjective evaluation is the gold standard to evaluate speech qualityoptimized for human perception. Perceptual objective metrics serve as a proxyfor subjective scores. The conventional and widely used metrics require areference clean speech signal, which is unavailable in real recordings. Theno-reference approaches correlate poorly with human ratings and are not widelyadopted in the research community. One of the biggest use cases of theseperceptual objective metrics is to evaluate noise suppression algorithms. Thispaper introduces a multi-stage self-teaching based perceptual objective metricthat is designed to evaluate noise suppressors. The proposed method generalizeswell in challenging test conditions with a high correlation to human ratings.

DNSMOS: A Non-Intrusive Perceptual Objective Speech Quality metric to evaluate Noise Suppressors