https://scholars.lib.ntu.edu.tw/handle/123456789/633096
標題: | MBI-Net: A Non-Intrusive Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids | 作者: | Zezario, Ryandhimas E. Chen, Fei CHIOU-SHANN FUH Wang, Hsin Min Tsao, Yu |
關鍵字: | cross-domain features | hearing aid | hearing loss | self-supervised learning | speech intelligibility | 公開日期: | 1-一月-2022 | 卷: | 2022-September | 來源出版物: | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH | 摘要: | 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/633096 | ISSN: | 2308457X | DOI: | 10.21437/Interspeech.2022-10838 |
顯示於: | 資訊工程學系 |
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