Multilingual Detection of Alzheimer's Disease from Speech: A Cross-Linguistic Transfer Learning Approach
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Computer Science > Computation and Language
Title:Multilingual Detection of Alzheimer's Disease from Speech: A Cross-Linguistic Transfer Learning Approach
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.
| Comments: | 5 pages |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2606.05545 [cs.CL] |
| (or arXiv:2606.05545v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.05545
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Emmanuel Akinrintoyo [view email][v1] Thu, 4 Jun 2026 00:59:05 UTC (466 KB)
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