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.
基于语音的多语言阿尔茨海默病检测：一种跨语言迁移学习方法 / Multilingual Detection of Alzheimer's Disease from Speech: A Cross-Linguistic Transfer Learning Approach
本文提出一种跨语言迁移学习方法，通过训练一个能够同时识别英语、中文、阿拉伯语和印地语中阿尔茨海默病语音特征的模型，实现了无需为每种语言单独训练即可高效检测疾病，平均F1分数达到82%，且推理时间仅需0.5秒，为全球范围内的快速筛查提供了可行性方案。
源自 arXiv: 2606.05545
