Paper Info
Title
A NEW MASK-BASED OBJECTIVE MEASURE FOR PREDICTING THE INTELLIGIBILITY OF BINARY MASKED SPEECH.
Abstract
Mask-based objective speech-intelligibility measures have been successfully proposed for evaluating the performance of binary masking algorithms. These objective measures were computed directly by comparing the estimated binary mask against the ground truth ideal binary mask (IdBM). Most of these objective measures, however, assign equal weight to all time-frequency (T-F) units. In this study, we propose to improve the existing mask-based objective measures by weighting each T-F unit according to its target or masker loudness. The proposed objective measure shows significantly better performance than two other existing mask-based objective measures.
Year
DOI
Venue
2013
10.1109/ICASSP.2013.6639025
ICASSP
Keywords
Field
DocType
objective measure,binary mask estimation,binary masked speech,binary masking algorithms,speech intelligibility,speech separation,idbm,mask-based objective speech-intelligibility measures,ideal binary mask,mask-based objective measure,bioinformatics,noise,measurement uncertainty,speech,biomedical research,spectrogram,time frequency analysis
Loudness,Weighting,Pattern recognition,Masking (art),Computer science,Speech recognition,Ground truth,Artificial intelligence,Binary number,Intelligibility (communication)
Conference
ISSN
Citations 
PageRank 
1520-6149
0
0.34
References 
Authors
3
4
Name
Order
Citations
PageRank
Chengzhu Yu1885.10
Kamil K. Wójcicki2394.22
Philipos C. Loizou399171.00
John H. L. Hansen43215365.75