Title
SNR loss: A new objective measure for predicting the intelligibility of noise-suppressed speech
Abstract
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.
Year
DOI
Venue
2011
10.1016/j.specom.2010.10.005
Speech Communication
Keywords
Field
DocType
critical-band spectral representation,snr loss,spectral amplification distortion,new objective measure,processed speech,spectral distortion,proposed measure,enhancement algorithm,noisy condition,spectral attenuation,speech enhancement algorithm,noise-suppressed speech,speech intelligibility,bioinformatics,biomedical research
Speech enhancement,Consonant,Pattern recognition,Critical band,Computer science,Speech recognition,Correlation,Vowel,Artificial intelligence,Attenuation,Spectral representation,Intelligibility (communication)
Journal
Volume
Issue
ISSN
53
3
Speech Communication
Citations 
PageRank 
References 
31
1.60
15
Authors
2
Name
Order
Citations
PageRank
Jianfen Ma1311.60
Philipos C. Loizou299171.00