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

TL;DR

This paper introduces MBI-Net, a multi-branched deep learning model designed to accurately predict speech intelligibility scores for hearing aid users, aiming to replace costly subjective listening tests.

Contribution

The paper presents a novel multi-branched neural network architecture that combines hearing loss modeling and feature extraction to improve speech intelligibility prediction accuracy.

Findings

MBI-Net outperforms baseline models on Clarity Prediction Challenge 2022 dataset.

The model effectively integrates multi-channel speech processing.

Experimental results demonstrate higher prediction scores than existing methods.

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.