中文版 | English
Title

MBI-Net: A Non-Intrusive Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids

Author
DOI
Publication Years
2022
Conference Name
Interspeech Conference
ISSN
2308-457X
EISSN
1990-9772
Source Title
Volume
2022-September
Pages
3944-3948
Conference Date
SEP 18-22, 2022
Conference Place
null,Incheon,SOUTH KOREA
Publication Place
C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE
Publisher
Abstract
Improving the user's hearing ability to understand speech in noisy environments is critical to the development of hearing aid (HA) devices. For this, it is important to derive a metric that can fairly predict speech intelligibility for HA users. A straightforward approach is to conduct a subjective listening test and use the test results as an evaluation metric. However, conducting large-scale listening tests is time-consuming and expensive. Therefore, several evaluation metrics were derived as surrogates for subjective listening test results. In this study, we propose a multi-branched speech intelligibility prediction model (MBI-Net), for predicting the subjective intelligibility scores of HA users. MBI-Net consists of two branches of models, with each branch consisting of a hearing loss model, a cross-domain feature extraction module, and a speech intelligibility prediction model, to process speech signals from one channel. The outputs of the two branches are fused through a linear layer to obtain predicted speech intelligibility scores. Experimental results confirm the effectiveness of MBI-Net, which produces higher prediction scores than the baseline system in Track 1 and Track 2 on the Clarity Prediction Challenge 2022 dataset.
Keywords
SUSTech Authorship
Others
Language
English
URL[Source Record]
Indexed By
WOS Research Area
Acoustics ; Audiology & Speech-Language Pathology ; Computer Science ; Engineering
WOS Subject
Acoustics ; Audiology & Speech-Language Pathology ; Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS Accession No
WOS:000900724504024
Scopus EID
2-s2.0-85140044400
Data Source
Scopus
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/406919
Affiliation
1.National Taiwan University,Taiwan
2.Academia Sinica,
3.Southern University of Science and Technology of China,China
Recommended Citation
GB/T 7714
Zezario,Ryandhimas E.,Chen,Fei,Fuh,Chiou Shann,et al. MBI-Net: A Non-Intrusive Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids[C]. C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE:ISCA-INT SPEECH COMMUNICATION ASSOC,2022:3944-3948.
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