标题： 非侵入式助听器可懂度预测：最新进展、趋势与挑战 显示英文标题

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

摘要： 本文概述了助听器（HA）非侵入性语音可懂度预测的最新进展。 我们总结了鲁棒声学特征提取、听力损失建模以及用于长序列处理的新兴架构的发展。 还讨论了旨在提高在未见过的声学环境中的鲁棒性的听众特定适应策略和领域泛化方法。 承认剩余挑战，例如需要大规模、多样化的数据集以及可靠的跨配置泛化。 我们的目标是提供对当前趋势、持续挑战以及向实用且可靠的面向HA的可懂度预测系统可能未来方向的观点。 显示英文摘要

摘要： 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.