用跨语言迁移学习，一模型多语言筛查阿尔茨海默病。
Multilingual Detection of Alzheimer's Disease from Speech: A Cross-Linguistic Transfer Learning Approach
- 用跨语言训练，让模型在未见过的语言上也能识别阿尔茨海默病。
- 在英、中、阿、印四种语言上均达到82%的F1分数。
- 推理仅需0.5秒，适合实时筛查，可全球部署。
由于语言特异性模型训练成本高且耗时，开发多语言阿尔茨海默病痴呆（AD）检测模型面临巨大挑战。本文提出一种基于跨语言训练的新方法，可在训练语言之外的语言中检测AD。研究利用英语、中文、阿拉伯语和印地语数据集，构建了基于Transformer的二分类模型，用于检测不同语言及认知障碍程度下的AD。该方法在所有语言上均实现82%的F1分数，展现出强大的跨语言泛化能力。模型推理时间仅0.5秒，支持潜在的实时筛查应用，且各语言间性能稳定，表明其具备全球部署可行性。
原文摘要 · Abstract (English)
The development of multilingual Alzheimer's Disease Dementia (AD) detection models presents significant challenges due to the resource-intensive and time-consuming nature of language-specific model training. We propose a novel solution using cross-language training to detect AD in languages beyond those used for model training. This study investigates multilingual deep learning models for detecting AD across different languages and cognitive impairment levels. Using datasets in English, Chinese, Arabic, and Hindi, we developed transformer-based models for binary AD classification. Our approach achieved F1 scores of 82\% across all languages, demonstrating strong cross-linguistic generalization. The rapid inference time (0.5 seconds) supports potential real-time screening applications, while consistent performance across languages indicates feasibility for global deployment.
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