@inproceedings{choi-etal-2024-crosslinguistic,
    title = "Crosslinguistic Acoustic Feature-based Dementia Classification Using Advanced Learning Architectures",
    author = "Choi, Anna Seo Gyeong  and
      Kim, Jin-seo  and
      Kim, Seo-hee  and
      Back, Min Seok  and
      Cho, Sunghye",
    editor = "Kokkinakis, Dimitrios  and
      Fraser, Kathleen C.  and
      Themistocleous, Charalambos K.  and
      Fors, Kristina Lundholm  and
      Tsanas, Athanasios  and
      Ohman, Fredrik",
    booktitle = "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",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.rapid-1.11",
    pages = "95--100",
    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.",
}

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.
Choi, Anna Seo Gyeong Kim, Jin-seo Kim, Seo-hee Back, Min Seok Cho, Sunghye
Crosslinguistic Acoustic Feature-based Dementia Classification Using Advanced Learning Architectures