Paper Info
Title
Improving Speech Intelligibility in Noise Using a Binary Mask That Is Based on Magnitude Spectrum Constraints
Abstract
A new binary mask is introduced for improving speech intelligibility based on magnitude spectrum constraints. The proposed binary mask is designed to retain time-frequency (T-F) units of the mixture signal satisfying a magnitude constraint while discarding T-F units violating the constraint. Motivated by prior intelligibility studies of speech synthesized using the ideal binary mask, an algorithm is proposed that decomposes the input signal into T-F units and makes binary decisions, based on a Bayesian classifier, as to whether each T-F unit satisfies the magnitude constraint or not. Speech corrupted at low signal-to-noise (SNR) levels (-5 and 0 dB) using different types of maskers is synthesized by this algorithm and presented to normal-hearing listeners for identification. Results indicated substantial improvements in intelligibility over that attained by human listeners with unprocessed stimuli.
Year
DOI
Venue
2010
10.1109/LSP.2010.2087412
IEEE Signal Process. Lett.
Keywords
Field
DocType
time-frequency units,speech intelligibility,bayes methods,magnitude spectrum constraints,binary mask,bayesian classifier,normal-hearing listeners,speech enhancement,satisfiability,time frequency,spectrum,noise measurement,speech,signal to noise ratio
Speech enhancement,Phase spectrum,Magnitude (mathematics),Noise measurement,Naive Bayes classifier,Computer science,Signal-to-noise ratio,Speech recognition,Intelligibility (communication),Binary number
Journal
Volume
Issue
ISSN
17
12
1070-9908
Citations 
PageRank 
References 
4
0.42
3
Authors
2
Name
Order
Citations
PageRank
Gibak Kim11037.38
Philipos C. Loizou299171.00