Using a single-channel reference with the MBSTOI binaural intelligibility metric
File(s)
Published version
Author(s)
Guiraud, Pierre
Moore, Alastair H
Vos, Rebecca R
Naylor, Patrick A
Brookes, Mike
Type
Journal Article
Abstract
In order to assess the intelligibility of a target signal in a noisy environment, intrusive speech intelligibility metrics are typically used. They require a clean reference signal to be available which can be difficult to obtain especially for binaural metrics like the modified binaural short time objective intelligibility metric (MBSTOI). We here present a hybrid version of MBSTOI that incorporates a deep learning stage that allows the metric to be computed with only a single-channel clean reference signal. The models presented are trained on simulated data containing target speech, localised noise, diffuse noise, and reverberation. The hybrid output metrics are then compared directly to MBSTOI to assess performances. Results show the performance of our single channel reference vs MBSTOI. The outcome of this work offers a fast and flexible way to generate audio data for machine learning (ML) and highlights the potential for low level implementation of ML into existing tools.
Date Issued
2023-04
Date Acceptance
2023-03-06
Citation
Speech Communication, 2023, 149, pp.74-83
ISSN
0167-6393
Publisher
Elsevier BV
Start Page
74
End Page
83
Journal / Book Title
Speech Communication
Volume
149
Copyright Statement
© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
http://dx.doi.org/10.1016/j.specom.2023.03.005
Publication Status
Published
Date Publish Online
2023-03-11