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Overview of speech quality metrics in terms of automated evaluation of signal denoising in a presence of non-stationary noise

Recent developments in neural network-based speech enhancement ask for a robust subjective metric that can be used for comparing performance of a different noise suppression algorithms (for non-stationary noises), that would closely match costly and time-consuming subjective user tests. The article describes results of a comparison between subjective scores obtained using MUSHRA methodology vs. automated evaluation with objective metrics i.e. POLQA, 3QUEST, STOI & ESTOI, on a set of recordings processed by 2 different denoising algorithms for close & far speaker distance. Correlation coefficient is calculated between subjective scores and examined metrics. The results are based on recordings simulated using an in-house simulation toolchain, based on impulse responses from actual laptop device used in low reverb quiet room.

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