  Human subjective evaluation is the gold standard to evaluate speech quality
optimized for human perception. Perceptual objective metrics serve as a proxy
for subjective scores. The conventional and widely used metrics require a
reference clean speech signal, which is unavailable in real recordings. The
no-reference approaches correlate poorly with human ratings and are not widely
adopted in the research community. One of the biggest use cases of these
perceptual objective metrics is to evaluate noise suppression algorithms. This
paper introduces a multi-stage self-teaching based perceptual objective metric
that is designed to evaluate noise suppressors. The proposed method generalizes
well in challenging test conditions with a high correlation to human ratings.
