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 vowel-consonant 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 high- or 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 high- or middle-level led to decreased intelligibility relative to noisy speech. This suggests that the lack of intelligibility improvement by existing noise-suppression algorithms is also present at segmental level, which may account for the deficit traditionally observed at full-sentence level.
Cite as: Guan, T., Chu, G., Chen, F., Yang, F. (2016) Assessing Level-Dependent Segmental Contribution to the Intelligibility of Speech Processed by Single-Channel Noise-Suppression Algorithms. Proc. Interspeech 2016, 122-125, doi: 10.21437/Interspeech.2016-43
@inproceedings{guan16_interspeech,
author={Tian Guan and Guangxing Chu and Fei Chen and Feng Yang},
title={{Assessing Level-Dependent Segmental Contribution to the Intelligibility of Speech Processed by Single-Channel Noise-Suppression Algorithms}},
year=2016,
booktitle={Proc. Interspeech 2016},
pages={122--125},
doi={10.21437/Interspeech.2016-43}
}