@inproceedings{8ac9be2527704eb9970add5af7c850b0,
title = "An End-to-End Model for Speech-based Somatisation Disorder Detection",
abstract = "Somatisation disorder is a chronic psychiatric disorder that often lacks medical explanation, which can cause more severe functional impairment and social difficulties. In non-invasive speech modalities, auxiliary diagnosis of somatisation disorder can be undertaken. In this contribution, we propose an end-to-end deep neural network that detects somatisation disorders from one-dimensional raw speech signals. Our study is based on the Shenzhen Somatisation Speech Corpus, using the Patient Health Questionnaire-15 as the evaluation scale. Moreover, ways to mitigate model overfitting are explored in this work. Our experimental results on the test set finally reach 58.4 % UAR in the binary classification task.",
keywords = "Affective Computing, Digital Health, End-to-End Learning, Somatisation Disorder Detection",
author = "Runze Ge and Zhihua Wang and Zhonghao Zhao and Kun Qian and Bin Hu and Schuller, {Bj{\"o}rn W.} and Yoshiharu Yamamoto",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 12th IEEE Global Conference on Consumer Electronics, GCCE 2023 ; Conference date: 10-10-2023 Through 13-10-2023",
year = "2023",
doi = "10.1109/GCCE59613.2023.10315568",
language = "English",
series = "GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "603--605",
booktitle = "GCCE 2023 - 2023 IEEE 12th Global Conference on Consumer Electronics",
address = "United States",
}