Towards Clinically Feasible Nonintrusive Quality and Intelligibility Indices for Hearing Aids

Speech quality and intelligibility are of significant importance during clinical hearing aid (HA) fitting and verification. Validated intrusive objective predictors of intelligibility and quality such as the Hearing Aid Speech Perception Index (HASPI) and the Hearing Aid Speech Quality Index (HASQI) have not been widely adopted for implementation within clinically available HA test systems. Recent advances in non-intrusive measures, such as those from Clarity Prediction Challenges (CPCs) and HASA-Net, are also not yet accessible to clinicians. Moreover, most of these advancements rely on datasets from simulated HAs, not the commercial devices used by audiologists. This work aims to develop non-intrusive quality and intelligibility indices using custom databases of noisy speech recorded in a HA test box. Similar to the successful models from previous CPCs, the indices were derived using a novel non-intrusive model leveraging automatic speech recognition and self-supervised learning techniques. The proposed non-intrusive model was trained to predict the intrusive HASPI/HASQI values and later validated against subjective intelligibility data obtained from a group of listeners with hearing loss. The proposed model resulted in strong correlations with HASQI (95.96%) and HASPI (97.59%), and a moderate correlation with the subjective intelligibility scores (76.63%).