Multilingual Detection of Alzheimer's Disease from Speech: A Cross-Linguistic Transfer Learning Approach
For researchers and clinicians needing scalable, cross-linguistic AD screening tools, this work demonstrates a viable transfer learning approach, though the performance is moderate and incremental over existing monolingual methods.
The paper addresses the challenge of developing multilingual Alzheimer's disease detection models from speech, achieving 82% F1 score across English, Chinese, Arabic, and Hindi with 0.5-second inference time.
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.