Modifying the normalized covariance metric measure to account for nonlinear distortions introduced by noise-reduction algorithms

Abstract

In this study, two methods are proposed to modify the normalized covariance metric (NCM) measure to reduce the effects of gain-induced nonlinear distortions introduced by most noise-suppression algorithms. Considering that the gain-induced distortions behave differently dependent on the signal-to-noise ratio between the noise-reduced speech and the noise, the first approach introduces a penalty factor involving this ratio in the modified NCM measure. The second approach deemphasizes segments marked with amplification distortions that contribute less to intelligibility via adaptive thresholding. Significantly higher correlations with intelligibility scores were obtained from the modified NCM measures compared with the original NCM measures.