@inproceedings{7b890fba82f14f5db1c0925b5b00419c,
title = "Detecting Depression in Speech Under Different Speaking Styles and Emotional Valences",
abstract = "Detecting depression in speech is a hot topic in recent years. Some inconsistent results in previous researches imply a few important influence factors are ignored. In this paper, we investigated a sample of 184 subjects (108 females, 76 males) to examine the influence of speaking style and emotional valence on depression detection. First, classification accuracy was used to measure the influence of these two factors. Then, two-way analysis of variance was employed to determine interactive acoustical features. Finally, normalized features by subtracting got higher classification accuracies. Results show that both speaking style and emotional valence are important factors. Spontaneous speech is better than automatic speech and neutral is the best choice among three emotional valences in depression detection. Normalized features improve the detection performance.",
keywords = "Depression, Emotional valence, Speaking style, Speech",
author = "Zhenyu Liu and Bin Hu and Xiaoyu Li and Fei Liu and Gang Wang and Jing Yang",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing AG.; International Conference on Brain Informatics, BI 2017 ; Conference date: 16-11-2017 Through 18-11-2017",
year = "2017",
doi = "10.1007/978-3-319-70772-3_25",
language = "English",
isbn = "9783319707716",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "261--271",
editor = "Yi Zeng and Bo Xu and Maryann Martone and Yong He and Hanchuan Peng and Qingming Luo and Kotaleski, {Jeanette Hellgren}",
booktitle = "Brain Informatics - International Conference, BI 2017, Proceedings",
address = "Germany",
}