Multilingual Detection of Alzheimer's Disease from Speech: A Cross-Linguistic Transfer Learning Approach
Quick Answer
This paper shows that A novel cross-language training approach enables multilingual detection of Alzheimer's Disease (AD) using transformer-based models, achieving 82% F1 scores across English, Chinese, Arabic, and Hindi.
Quick Take
A novel cross-language training approach enables multilingual detection of Alzheimer's Disease (AD) using transformer-based models, achieving 82% F1 scores across English, Chinese, Arabic, and Hindi. The models demonstrate rapid inference times of 0.5 seconds, supporting real-time screening applications globally.
Key Points
- Cross-language training allows AD detection in multiple languages without language-specific models.
- Transformer-based models achieved 82% F1 scores across four languages.
- Rapid inference time of 0.5 seconds enables potential real-time screening.
- Consistent performance across languages indicates feasibility for global deployment.
- Study utilizes datasets from English, Chinese, Arabic, and Hindi.
Article Excerpt
From source RSS / original summaryarXiv: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.
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