首个基于语音的帕金森早期检测基准，支持公平可复现的模型评估。
A Benchmark for Early-stage Parkinson's Disease Detection from Speech
- 构建跨方法可比的语音早期帕金森检测基准，采用说话人无关划分。
- 覆盖三种常见语音任务，在不同训练资源下验证模型性能。
- 提供按数据集、性别、疾病阶段等多维度评估结果，助力临床应用。
从语音中进行早期帕金森病（EarlyPD）检测具有重要临床意义但研究仍不充分，且现有成果难以比较，因各研究在数据集、语言、任务、评估协议和早期帕金森定义上存在差异。为解决此问题，我们提出首个基于语音的早期帕金森病检测基准，采用说话人无关划分，确保在研究人员可访问的数据集上实现公平、可复现的跨方法评估。该基准涵盖三种常见语音任务，并在不同训练资源设置下评估方法表现。同时，通过按数据集、聚合层级、性别和疾病阶段的多维评估分解，支持细粒度比较与临床采纳。我们的结果提供了可复现的参考和切实可行的洞察，鼓励采用这一公开基准，推动鲁棒且具临床意义的语音基早期帕金森检测发展。
原文摘要 · Abstract (English)
Early-stage Parkinson's disease (EarlyPD) detection from speech is clinically meaningful yet underexplored, and published results are hard to compare because studies differ in datasets, languages, tasks, evaluation protocols, and EarlyPD definitions. To address this issue, we propose the first benchmark for speech-based EarlyPD detection, with a speaker-independent split designed for fair and replicable cross-method evaluation on researcher-accessible datasets. The benchmark covers three common speech tasks and evaluates methods under different training-resource settings. We also present multi-dimensional evaluation breakdowns by dataset, aggregation level, gender, and disease stage to support fine-grained comparisons and clinical adoption. Our results provide a replicable reference and actionable insights, encouraging the adoption of this publicly available benchmark to advance robust and clinically meaningful speech-based EarlyPD detection.
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