  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.
