Intelligibility Prediction for Time-Modified Speech Signals Using Spectro-Temporal Modulation Features

Reference-based speech intelligibility prediction algorithms (RB-SIPAs) are limited to speech degradations that maintain the time alignment between the clean and degraded speech signals. We address this limitation by augmenting the existing RB-SIPAs framework with time alignment. To achieve robust time alignment at low signal-to-noise ratios, we propose using spectro-temporal modulations (STM) of the speech signals for dynamic time warping (DTW). Our experiments demonstrate that in DTW, the use of specific STM components as features for clean and time-modified degraded speech achieves the baseline time alignment obtained using clean speech and noise-free time-modified speech. Moreover, we propose two methods to incorporate time alignment into existing RB-SIPAs. Using these methods, we compare the output scores of the RB-SIPA with listening test scores and show better correlation results using the STM features as compared to MFCCs.