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 m
Advances in transformer models and increasing availability of multilingual datasets are enabling more sophisticated cross-linguistic applications in AI.
This development allows for broader and more equitable access to advanced diagnostic tools, reducing the inherent biases and resource intensities of language-specific AI models.
The ability to accurately detect diseases like Alzheimer's across multiple languages with a single model changes the paradigm for global health diagnostics, especially in resource-constrained environments.
- · Global healthcare systems
- · AI diagnostic developers
- · Patients in non-English speaking regions
- · Linguistic minorities
- · Developers of single-language diagnostic models
- · Companies relying on language-specific data monopolies
More widespread and accessible early detection of neurological disorders globally.
Reduced healthcare costs associated with specialized diagnostic infrastructure for each language.
Accelerated development of other multilingual AI applications in medical and psychological fields due to proven transfer learning efficacy.
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