Segmental contribution to predicting speech intelligibility in noisy conditions

Lei Wang, Fei Chen, Ying Hui Lai

研究成果: Conference contribution同行評審

摘要

It is necessary to identify speech segments carrying important information for speech intelligibility, particularly in noise. Earlier work based on a relative rootmean-square (RMS) level based segmentation suggested that middle-level (ranging from the overall RMS level to 10 dB below) segments contained more vowel-consonant boundaries wherein the spectral change was often most prominent, and perhaps most robust, in the presence of noise, and hence yielded improved performance of objective intelligibility modeling. Since the three levels (i.e., high-, middle- and low-levels) were defined empirically when proposed, the present work assessed how the boundaries of RMS-level based segmentation affected the performance of speech intelligibility prediction. When evaluated with speech recognition scores obtained with normal-hearing listeners and with a total of 72 noisedistorted and noise-suppressed conditions, it was shown that choosing 0 and - 10 dB to split middle-level led to maximized correlation in predicting the intelligibility of speech in noise.

原文English
主出版物標題Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016
發行者Institute of Electrical and Electronics Engineers Inc.
頁面476-480
頁數5
ISBN(電子)9781509021789
DOIs
出版狀態Published - 16 8月 2016
事件2nd IEEE International Conference on Multimedia Big Data, BigMM 2016 - Taipei, Taiwan
持續時間: 20 4月 201622 4月 2016

出版系列

名字Proceedings - 2016 IEEE 2nd International Conference on Multimedia Big Data, BigMM 2016

Conference

Conference2nd IEEE International Conference on Multimedia Big Data, BigMM 2016
國家/地區Taiwan
城市Taipei
期間20/04/1622/04/16

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