Abstract
Although requiring prior knowledge makes the ideal binary mask an impractical algorithm, substantial
increases in measured intelligibility make it a desirable benchmark. While this benchmark
has been studied extensively, many questions remain about the factors that influence the intelligibility
of binary-masked speech with non-ideal masks. To date, researchers have used primarily uniformly
random, uncorrelated mask errors and independently presented error types (i.e., false
positives and negatives) to characterize the influence of estimation errors on intelligibility.
However, practical estimation algorithms produce masks that contain errors of both types and with
non-trivial amounts of structure. This paper introduces an investigation framework for binary masks
and presents listener studies that use this framework to illustrate how interactions between error
types and structure affect intelligibility. First, this study demonstrates that clustering (i.e., a form of
structure) of mask errors reduces intelligibility. Furthermore, while previous research has suggested
that false positives are more detrimental to intelligibility than false negatives, this study indicates
that false negatives can be equally detrimental to intelligibility when they contain structure or when
both error types are present. Finally, this study shows that listeners tolerate fewer mask errors when
both types of errors are present, especially when the errors contain structure.
Original language | English |
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Journal | Journal of the Acoustical Society of America |
Volume | 137 |
Issue number | 4 |
Pages (from-to) | 2025–2035 |
ISSN | 0001-4966 |
DOIs | |
Publication status | Published - 2015 |
Externally published | Yes |