Assessing Level-Dependent Segmental Contribution to the Intelligibility of Speech Processed by Single-Channel Noise-Suppression Algorithms

Most existing single-channel noise-suppression algorithms cannot improve speech intelligibility for normal-hearing listeners; however, the underlying reason for this performance deficit is still unclear. Given that various speech segments contain different perceptual contributions, the present work assesses whether the intelligibility of noisy speech can be improved when selectively suppressing its noise at high-level (vowel-dominated) or middle-level (containing vowelconsonant transitions) segments by existing single-channel noise-suppression algorithms. The speech signal was corrupted by speech-spectrum shaped noise and two-talker babble masker, and its noisy highor middle-level segments were replaced by their noise-suppressed versions processed by four types of existing single-channel noise-suppression algorithms. Experimental results showed that performing segmental noise-suppression at highor middle-level led to decreased intelligibility relative to noisy speech. This suggests that the lack of intelligibility improvement by existing noisesuppression algorithms is also present at segmental level, which may account for the deficit traditionally observed at full-sentence level.