不侵入式语音可懂度预测助力助听器精准适配
Non-Intrusive Intelligibility Prediction for Hearing Aids: Recent Advances, Trends, and Challenges
- 通过声学特征与听力损失建模，实现无侵入式可懂度评估
- 新架构提升长序列语音处理能力，增强环境适应性
- 适合助听器研发者与听力康复研究者参考
本文综述了助听器领域非侵入式语音可懂度预测的最新进展。重点总结了鲁棒声学特征提取、听力损失建模以及面向长序列处理的新兴网络架构的发展。同时讨论了针对个体用户差异的自适应策略和旨在提升在未见声学环境下泛化能力的域泛化方法。文章指出仍存在大规模、多样化的数据集不足，以及跨用户配置可靠泛化能力欠缺等挑战。目标是为当前趋势、持续挑战及未来实用化、可靠化的助听器导向可懂度预测系统提供展望。
原文摘要 · Abstract (English)
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.
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