# Non-Intrusive Intelligibility Prediction for Hearing Aids: Recent Advances, Trends, and Challenges

arXiv: https://arxiv.org/abs/2509.03017 · HTML: https://mlsanity.com/paper/2509.03017/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2025.

## Built on by (most cited first, 1 of 1)

- [Leveraging Multiple Speech Enhancers for Non-Intrusive Intelligibility Prediction for Hearing-Impaired Listeners](https://mlsanity.com/paper/2509.16979/) (2025)

## Abstract (arXiv)

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.