{"ID":2874876,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.03017","arxiv_id":"2509.03017","title":"Non-Intrusive Intelligibility Prediction for Hearing Aids: Recent Advances, Trends, and Challenges","abstract":"This paper provides an overview of recent progress in non-intrusive speech intelligibility prediction for hearing aids (HA). We summarize developments in robust acoustic feature extraction, hearing loss modeling, and the use of emerging architectures for long-sequence processing. Listener-specific adaptation strategies and domain generalization approaches that aim to improve robustness in unseen acoustic environments are also discussed. Remaining challenges, such as the need for large-scale, diverse datasets and reliable cross-profile generalization, are acknowledged. Our goal is to offer a perspective on current trends, ongoing challenges, and possible future directions toward practical and reliable HA-oriented intelligibility prediction systems.","short_abstract":"This paper provides an overview of recent progress in non-intrusive speech intelligibility prediction for hearing aids (HA). We summarize developments in robust acoustic feature extraction, hearing loss modeling, and the use of emerging architectures for long-sequence processing. Listener-specific adaptation strategies...","url_abs":"https://arxiv.org/abs/2509.03017","url_pdf":"https://arxiv.org/pdf/2509.03017v1","authors":"[\"Ryandhimas E. Zezario\"]","published":"2025-09-03T04:49:08Z","proceeding":"eess.AS","tasks":"[\"eess.AS\",\"cs.SD\"]","methods":"[]","has_code":false}