用检索方式评估语音症状，保护隐私还能轻松更新数据。
Adaptable Non-parametric Approach for Speech-based Symptom Assessment: Isolating Private Medical Data in a Retrieval Datastore
- 不训练模型参数，通过检索医疗语音数据做症状判断。
- 在多个数据集上表现接近微调方法，且更新数据更快更安全。
- 适合需要保护患者隐私、频繁更新的医疗健康应用。
基于语音的健康状态自动评估有望提升医疗可及性与可负担性。尽管参数化模型有潜力，但面临隐私和适应性挑战。为此，我们提出非参数化语音症状评估框架（NoNPSA）。通过将医疗数据隔离于检索数据存储中，NoNPSA避免将私密信息编码进模型参数，并支持高效的数据更新。采用在通用数据集上预训练的自监督学习（SSL）模型提取特征，用于相似性检索。元数据感知的精炼机制筛选检索结果，结合关联标签计算评估分数。实验表明，NoNPSA性能媲美基于微调的SSL方法，同时具备更强隐私保护、更高的更新效率和更好的适应性，展示了非参数方法在医疗领域的潜力。
原文摘要 · Abstract (English)
The automatic assessment of health-related acoustic cues has the potential to improve healthcare accessibility and affordability. Although parametric models are promising, they face challenges in privacy and adaptability. To address these, we propose a NoN-Parametric framework for Speech-based symptom Assessment (NoNPSA). By isolating medical data in a retrieval datastore, NoNPSA avoids encoding private information in model parameters and enables efficient data updates. A self-supervised learning (SSL) model pre-trained on general-purpose datasets extracts features, which are used for similarity-based retrieval. Metadata-aware refinement filters the retrieved data, and associated labels are used to compute an assessment score. Experimental results show that NoNPSA achieves competitive performance compared to fine-tuning SSL-based methods, while enabling greater privacy, update efficiency, and adaptability--showcasing the potential of non-parametric approaches in healthcare.
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