SOBM - a binary mask for noisy speech that optimises an objective intelligibility metric
File(s)
Accepted version
Author(s)
Lightburn, L
Brookes, M
Type
Conference Paper
Abstract
It is known that the intelligibility of noisy speech can be
improved by applying a binary-valued gain mask to a timefrequency
representation of the speech. We present the
SOBM, an oracle binary mask that maximises STOI, an
objective speech intelligibility metric. We show how to determine
the SOBM for a deterministic noise signal and also
for a stochastic noise signal with a known power spectrum.
We demonstrate that applying the SOBM to noisy speech results
in a higher predicted intelligibility than is obtained with
other masks and show that the stochastic version is robust to
mismatch errors in SNR and noise spectrum.
improved by applying a binary-valued gain mask to a timefrequency
representation of the speech. We present the
SOBM, an oracle binary mask that maximises STOI, an
objective speech intelligibility metric. We show how to determine
the SOBM for a deterministic noise signal and also
for a stochastic noise signal with a known power spectrum.
We demonstrate that applying the SOBM to noisy speech results
in a higher predicted intelligibility than is obtained with
other masks and show that the stochastic version is robust to
mismatch errors in SNR and noise spectrum.
Date Issued
2015-04-24
Date Acceptance
2015-01-14
Citation
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015, pp.5078-5082
ISSN
1520-6149
Publisher
IEEE
Start Page
5078
End Page
5082
Journal / Book Title
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Copyright Statement
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Source
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publication Status
Published
Start Date
2015-04-19
Finish Date
2015-04-24
Coverage Spatial
Brisbane, Australia