基于多模态语音的阿尔茨海默病检测的鲁棒排名聚合

中文摘要 AI 辅助

基于语音的阿尔茨海默病（AD）检测近期受益于多模态基础模型表示，这些表示整合了互补的声学和语言信息。然而，对这些互补分类器进行传统的概率平均并不可靠，因为它们的后验概率存在尺度不匹配：相同的数值可能在不同模型中反映不同的置信水平。我们提出了一种鲁棒的排名聚合框架，该框架聚合归一化的预测排名而非后验概率。每个受试者根据其在固定训练队列分布中的折外预测的百分位数进行评分；由于排名顺序对单调变换具有不变性，这避免了概率尺度不匹配，同时保留了分类器的置信度排序。一个置信门控的随机森林进一步利用临床可解释的语言特征来纠正残余错误，仅在排名预测与随机森林预测不一致且随机森林高度自信时覆盖排名预测，无需额外的深度模型训练或显式的后验概率校准。在ADReSS2020和ADReSSo2021上，该方法分别达到了95.83%和90.14%的准确率，与先前报道的结果相比表现优异。

英文摘要

Speech-based Alzheimer's disease (AD) detection has recently benefited from multimodal foundation-model representations that integrate complementary acoustic and linguistic information. However, conventional probability averaging over these complementary classifiers is unreliable, because their posterior probabilities exhibit mismatched scales: identical values may reflect different confidence levels across models. We propose a robust rank aggregation framework that aggregates normalized prediction ranks instead of posterior probabilities. Each subject is scored by its percentile within a fixed training-cohort distribution of out-of-fold predictions; since rank ordering is invariant to monotonic transformations, this avoids probability-scale mismatch while preserving classifier confidence ordering. A confidence-gated Random Forest further corrects residual errors using clinically interpretable linguistic features, overriding the rank prediction only when the two disagree and the RF is highly confident, without additional deep model training or explicit posterior-probability calibration. On ADReSS2020 and ADReSSo2021, the method achieves accuracies of 95.83% and 90.14%, respectively, comparing favorably with previously reported results.

发表机构

• The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
• Center for Language, Intelligence and Machines (LIMA), Shenzhen Loop Area Institute (SLAI)（深圳环区研究所（SLAI）语言、智能与机器中心（LIMA））

机构由 AI 辅助整理，请以论文原文为准。

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5 pages, 4 figures, 2 tables