Min Seok Back


2024

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Crosslinguistic Acoustic Feature-based Dementia Classification Using Advanced Learning Architectures
Anna Seo Gyeong Choi | Jin-seo Kim | Seo-hee Kim | Min Seok Back | Sunghye Cho
Proceedings of the Fifth Workshop on Resources and ProcessIng of linguistic, para-linguistic and extra-linguistic Data from people with various forms of cognitive/psychiatric/developmental impairments @LREC-COLING 2024

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.