Title | MBI-Net: A Non-Intrusive Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids |
Author | |
DOI | |
Publication Years | 2022
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Conference Name | Interspeech Conference
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ISSN | 2308-457X
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EISSN | 1990-9772
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Source Title | |
Volume | 2022-September
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Pages | 3944-3948
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Conference Date | SEP 18-22, 2022
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Conference Place | null,Incheon,SOUTH KOREA
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Publication Place | C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE
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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
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Language | English
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URL | [Source Record] |
Indexed By | |
WOS Research Area | Acoustics
; Audiology & Speech-Language Pathology
; Computer Science
; Engineering
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WOS Subject | Acoustics
; Audiology & Speech-Language Pathology
; Computer Science, Artificial Intelligence
; Engineering, Electrical & Electronic
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WOS Accession No | WOS:000900724504024
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Scopus EID | 2-s2.0-85140044400
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Data Source | Scopus
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Citation statistics |
Cited Times [WOS]:0
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Document Type | Conference paper |
Identifier | http://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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