It has always been challenging to non-intrusively predict speech intelligibility without the access of clean reference speech signal. This is extremely so in the scenario when predicting the intelligibility of noise-suppressed speech because of the non-linear distortion introduced by noise-suppression algorithms. The purpose of this study was to assess the segmental contribution to the non-intrusive intelligibility prediction of noise-suppressed speech by using the speech-to-reverberation-modulation-energy-ratio (SRMR) measure. To do this, speech signal was first split into different segments based on the relative root-mean-square level, and selected (e.g., all, high-level or middle-level) segments were used to compute the SRMR measure. The SRMR measures were correlated with the subjective intelligibility scores collected from human listeners, including a total of 72 conditions with linear and non-linear distortions. Experimental data showed that while the SRMR measure could not well model the intelligibility of noise-suppressed speech (i.e., correlation coefficient 0.37), the high-level-based SRMR measure may yield the correlation comparable to that computed with all segments. This indicates that high-level segments contain information accounting for the non-intrusive intelligibility prediction traditionally obtained with all segments.
['Lei Wang', 'Fei Chen']
Assessing the segmental contribution to the non-intrusive intelligibility prediction of noise-suppressed speech