Multilingual Detection of Alzheimer's Disease from Speech: A Cross-Linguistic Transfer Learning Approach

2026-06-03

AI Summary

This study addresses the challenges of data scarcity and high annotation costs in multilingual Alzheimer’s disease detection from speech by proposing a novel cross-lingual transfer learning paradigm that requires no labeled data in the target language. Built upon a Transformer architecture, the model is trained exclusively on English, Chinese, Arabic, and Hindi speech data yet demonstrates strong generalization to unseen languages. Experimental results show that the approach achieves an average F1 score of 82% across all tested languages, with each inference taking only 0.5 seconds. These findings highlight the method’s exceptional cross-lingual generalization capability and its potential for real-time, scalable screening applications in diverse linguistic settings.

Abstract

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.

Problem

• Alzheimer's Disease
• multilingual detection
• cross-linguistic
• speech-based diagnosis
• dementia screening

Innovation

• cross-linguistic transfer learning
• multilingual Alzheimer's detection
• transformer-based model
• speech-based diagnosis
• real-time screening