Document Detail

SNR Loss: A new objective measure for predicting speech intelligibility of noise-suppressed speech.
MedLine Citation:
PMID:  21503274     Owner:  NLM     Status:  Publisher    
Most of the existing intelligibility measures do not account for the distortions present in processed speech, such as those introduced by speech-enhancement algorithms. In the present study, we propose three new objective measures that can be used for prediction of intelligibility of processed (e.g., via an enhancement algorithm) speech in noisy conditions. All three measures use a critical-band spectral representation of the clean and noise-suppressed signals and are based on the measurement of the SNR loss incurred in each critical band after the corrupted signal goes through a speech enhancement algorithm. The proposed measures are flexible in that they can provide different weights to the two types of spectral distortions introduced by enhancement algorithms, namely spectral attenuation and spectral amplification distortions. The proposed measures were evaluated with intelligibility scores obtained by normal-hearing listeners in 72 noisy conditions involving noise-suppressed speech (consonants and sentences) corrupted by four different maskers (car, babble, train and street interferences). Highest correlation (r=-0.85) with sentence recognition scores was obtained using a variant of the SNR loss measure that only included vowel/consonant transitions and weak consonant information. High correlation was maintained for all noise types, with a maximum correlation (r=-0.88) achieved in street noise conditions.
Jianfen Ma; Philipos C Loizou
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Publication Detail:
Journal Detail:
Title:  Speech communication     Volume:  53     ISSN:  0167-6393     ISO Abbreviation:  -     Publication Date:  2011 Mar 
Date Detail:
Created Date:  2011-4-19     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8309707     Medline TA:  Speech Commun     Country:  -    
Other Details:
Languages:  ENG     Pagination:  340-354     Citation Subset:  -    
Taiyuan University of Technology, Shanxi, 030024, China, and Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX 75083.
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MeSH Terms
Grant Support
R01 DC010494-01A1//NIDCD NIH HHS

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