aiqbench analysis
This research demonstrates the viability of cross-linguistic transfer learning for deploying Alzheimer's detection models globally, achieving consistent 82% F1 scores across English, Chinese, Arabic, and Hindi with sub-second inference. By mitigating the need for language-specific training data, the approach offers a scalable pathway for real-time screening in diverse linguistic environments. The study validates transformer-based architectures as effective tools for addressing resource constraints in multilingual healthcare applications.
Multilingual Detection of Alzheimer's Disease from Speech: A Cross-Linguistic Transfer Learning Approach
arXiv:2606.05545v1 Announce Type: new 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.
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.
Computation and Language (cs.CL)
arXiv:2606.05545 [cs.CL]
(or arXiv:2606.05545v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.05545 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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