@inproceedings{d20608c1442e452e8b60f5472753128d,
title = "Evaluation of depression severity in speech",
abstract = "Depression is a frequent affective disorder, leading to a high impact on patients, their families and society. Depression diagnosis is limited by assessment methods that rely on patient-reported or clinician judgments of symptom severity. Recently, many researches showed that voice is an objective indicator for depressive diagnosis. In this paper, we investigate a sample of 111 subjects (38 healthy controls, 36 mild depressed patients and 37 severe depressed patients) through comparative analysis to explore the correlation between acoustic features and depression severity. We extract features as many as possible according to previous researches to create a large voice feature set. Then we employ some feature selection methods to form compact subsets on different tasks. Finally, we evaluate depressive disorder severity by these acoustic feature subsets. Results show that interview is a better choice than reading and picture description for depression assessment. Meanwhile, speech signal correlate to depression severity in a medium-level with statistically significant (p < 0.01).",
keywords = "Acoustic feature, Depression severity, Feature selection, PHQ-9, Speech",
author = "Zhenyu Liu and Bin Hu and Fei Liu and Huanyu Kang and Xiaoyu Li and Lihua Yan and Tianyang Wang",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; International Conference on Brain Informatics and Health, BIH 2016 ; Conference date: 13-10-2016 Through 16-10-2016",
year = "2016",
doi = "10.1007/978-3-319-47103-7_31",
language = "English",
isbn = "9783319471020",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "312--321",
editor = "Hesham Ali and Yong Shi and Ascoli, {Giorgio A.} and Deepak Khazanchi and Michael Hawrylycz",
booktitle = "Brain Informatics and Health - International Conference, BIH 2016, Proceedings",
address = "Germany",
}