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

Zezario，Ryandhimas E.1,2; Chen，Fei3; Fuh，Chiou Shann1; Wang，Hsin Min2; Tsao，Yu2

10.21437/Interspeech.2022-10838

2022

Interspeech Conference

2308-457X

1990-9772

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

2022-September

3944-3948

SEP 18-22, 2022

null,Incheon,SOUTH KOREA

C/O EMMANUELLE FOXONET, 4 RUE DES FAUVETTES, LIEU DIT LOUS TOURILS, BAIXAS, F-66390, FRANCE

ISCA-INT SPEECH COMMUNICATION ASSOC

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.

cross-domain features hearing aid hearing loss self-supervised learning speech intelligibility

其他

英语

CPCI-S

Acoustics ; Audiology & Speech-Language Pathology ; Computer Science ; Engineering

Acoustics ; Audiology & Speech-Language Pathology ; Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic

WOS:000900724504024

2-s2.0-85140044400

Scopus

1.National Taiwan University,Taiwan
2.Academia Sinica,
3.Southern University of Science and Technology of China,China

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.