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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Crosslinguistic Acoustic Feature-based Dementia Classification Using Advanced Learning Architectures
%A Choi, Anna Seo Gyeong
%A Kim, Jin-seo
%A Kim, Seo-hee
%A Back, Min Seok
%A Cho, Sunghye
%Y Kokkinakis, Dimitrios
%Y Fraser, Kathleen C.
%Y Themistocleous, Charalambos K.
%Y Fors, Kristina Lundholm
%Y Tsanas, Athanasios
%Y Ohman, Fredrik
%S 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
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F choi-etal-2024-crosslinguistic
%X 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.
%U https://aclanthology.org/2024.rapid-1.11
%P 95-100

Markdown (Informal)

[Crosslinguistic Acoustic Feature-based Dementia Classification Using Advanced Learning Architectures](https://aclanthology.org/2024.rapid-1.11) (Choi et al., RaPID-WS 2024)

• Crosslinguistic Acoustic Feature-based Dementia Classification Using Advanced Learning Architectures (Choi et al., RaPID-WS 2024)

ACL

• Anna Seo Gyeong Choi, Jin-seo Kim, Seo-hee Kim, Min Seok Back, and Sunghye Cho. 2024. Crosslinguistic Acoustic Feature-based Dementia Classification Using Advanced Learning Architectures. In 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, pages 95–100, Torino, Italia. ELRA and ICCL.