Audiovisual joint learning for end-to-end hearing aids

Abstract

Speech understanding in noise remains challenging for hearing-aid users, particularly in the presence of competing speakers. Conventional hearing aids typically perform speech enhancement (SE) and hearing-loss compensation in separate stages, which may cause enhancement errors and signal distortions to carry over to the amplification stage. Moreover, audio-only SE often provides limited benefits under competing-speech conditions because the target and interfering speech share similar acoustic characteristics, making them difficult to separate. To address these limitations, we propose AV-NeuroAMP, an end-to-end audiovisual framework that integrates noisy speech, target-talker video, and the listener's audiogram to jointly perform SE, personalized amplification, and dynamic-range compression. We further introduce audiogram-conditioned feature-wise linear modulation (AC-FiLM) to effectively incorporate listener-specific hearing profiles. Objective evaluations showed that AV-NeuroAMP outperformed conventional amplification, the audio-only NeuroAMP model, and two-stage systems on an in-domain English test set, and that these improvements were retained on an unseen Mandarin test set. Listening tests involving normal-hearing participants under simulated hearing loss and listeners with hearing loss further demonstrated improvements in speech quality and intelligibility, with the greatest benefits observed under competing-speech conditions. These findings support end-to-end audiovisual personalized amplification as a promising approach for improving hearing-aid performance in challenging acoustic environments.