研究语音中与阿尔茨海默病相关的声学线索，发现不同语言性别间诊断与人耳感知不一致。
Disentangling Acoustic Cues in Alzheimer's Pathology and Perception: The Roles of Language and Gender
- 跨语言跨性别训练模型预测病理与听觉感知
- 中文女性组病理与感知高度一致，希腊男性组则完全不一致
- 揭示全局可解释性AI可能掩盖关键人群差异，需针对性审计
声学生物标志物在阿尔茨海默病（AD）检测中展现潜力，但其驱动诊断AI的声学线索是否与人类听者感知一致，尚未在不同语言和性别间充分探索。本文在汉语和希腊语、男性与女性说话者中训练模型，预测临床AD状态（病理）及人类感知评分。采用SHAP进行可解释性分析，结合统计模型验证特征重要性。结果表明：病理-感知一致性具有情境依赖性——在汉语和女性说话者中显著，而在希腊语和男性说话者中完全消失，病理模型表现仅达随机水平；这一失败模式暴露了群体特异性审计的必要性。全局可解释性AI解释可能掩盖关键人口差异，提示临床语音AI公平部署需引入人群特异性可解释性审计。
原文摘要 · Abstract (English)
Acoustic biomarkers show promise for detecting Alzheimer's Disease (AD), yet whether the cues driving diagnostic AI align with those salient to human listeners is underexplored across languages and genders, where pathological markers and perceptual strategies differ. We train models to predict clinical AD status (pathology) and human perceptual scores across Mandarin and Greek, male and female speakers. Using SHAP for interpretability and statistical models for validation, we compare feature importance by subgroup. Results reveal a context-dependent divergence: pathological-perceptual alignment is significant for Mandarin and female speakers but disappears for Greek and male speakers, where pathology models did not exceed chance; this is a failure mode that population-specific auditing surfaces. Global Explainable AI (XAI) explanations can mask critical demographic divergences, highlighting the need for population-specific explainability auditing for equitable deployment of clinical speech AI.
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