发表机构
University of Science and Technology of China; University of Edinburgh（中国科学技术大学; 爱丁堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究阿尔茨海默病中声学线索，训练模型预测普通话和希腊语、男女说话者的临床AD状态及人类感知分数，用SHAP和统计模型分析，发现病理与感知一致性因语言和性别而异，强调特定人群可解释性审核对临床语音AI公平部署的必要性。
AI 中文摘要
声学生物标志物有望用于检测阿尔茨海默病（AD），但驱动诊断性人工智能的线索与人类听众突出的线索是否一致，在语言和性别方面尚未得到充分探索，因为病理标记和感知策略存在差异。我们训练模型预测普通话和希腊语、男性和女性说话者的临床AD状态（病理学）和人类感知分数。使用SHAP进行可解释性分析，并使用统计模型进行验证，我们按亚组比较特征重要性。结果显示了一种依赖上下文的差异：病理与感知的一致性在普通话和女性说话者中显著，但在希腊语和男性说话者中消失，病理模型未超过随机水平；这是特定人群审核揭示的一种失败模式。全局可解释人工智能（XAI）解释可能掩盖关键的人口统计学差异，凸显了对特定人群可解释性审核的需求，以实现临床语音人工智能的公平部署。
英文摘要
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.
CommentsAccepted at Interspeech 2026
