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Crosslinguistic Acoustic Feature-based Dementia Classification Using Advanced Learning Architectures
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Crosslinguistic Acoustic Feature-based Dementia Classification Using Advanced Learning Architectures
In this study, we rigorously evaluated eight machine learning and deep learning classifiers for identifying Alzheimer’s Disease (AD) patients using crosslinguistic acoustic features automatically extracted from one-minute oral picture descriptions produced by speakers of American English, Korean, and Mandarin Chinese. We employed eGeMAPSv2 and ComParE feature sets on segmented and non-segmented audio data. The Multilayer Perceptron model showed the highest performance, achieving an accuracy of 83.54% and an AUC of 0.8 on the ComParE features extracted from non-segmented picture description data. Our findings suggest that classifiers trained with acoustic features extracted from one-minute picture description data in multiple languages are highly promising as a quick, language-universal, large-scale, remote screening tool for AD. However, the dataset included predominantly English-speaking participants, indicating the need for more balanced multilingual datasets in future research.
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