Detecting Depression Using an Ensemble Logistic Regression Model Based on Multiple Speech Features
Joint Authors
Wang, Gang
Jiang, Haihua
Hu, Bin
Liu, Zhenyu
Zhang, Lan
Li, Xiaoyu
Kang, Huanyu
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-09-24
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Early intervention for depression is very important to ease the disease burden, but current diagnostic methods are still limited.
This study investigated automatic depressed speech classification in a sample of 170 native Chinese subjects (85 healthy controls and 85 depressed patients).
The classification performances of prosodic, spectral, and glottal speech features were analyzed in recognition of depression.
We proposed an ensemble logistic regression model for detecting depression (ELRDD) in speech.
The logistic regression, which was superior in recognition of depression, was selected as the base classifier.
This ensemble model extracted many speech features from different aspects and ensured diversity of the base classifier.
ELRDD provided better classification results than the other compared classifiers.
A technique for identifying depression based on ELRDD, ELRDD-E, was here suggested and tested.
It offered encouraging outcomes, revealing a high accuracy level of 75.00% for females and 81.82% for males, as well as an advantageous sensitivity/specificity ratio of 79.25%/70.59% for females and 78.13%/85.29% for males.
American Psychological Association (APA)
Jiang, Haihua& Hu, Bin& Liu, Zhenyu& Wang, Gang& Zhang, Lan& Li, Xiaoyu…[et al.]. 2018. Detecting Depression Using an Ensemble Logistic Regression Model Based on Multiple Speech Features. Computational and Mathematical Methods in Medicine،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1132093
Modern Language Association (MLA)
Jiang, Haihua…[et al.]. Detecting Depression Using an Ensemble Logistic Regression Model Based on Multiple Speech Features. Computational and Mathematical Methods in Medicine No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1132093
American Medical Association (AMA)
Jiang, Haihua& Hu, Bin& Liu, Zhenyu& Wang, Gang& Zhang, Lan& Li, Xiaoyu…[et al.]. Detecting Depression Using an Ensemble Logistic Regression Model Based on Multiple Speech Features. Computational and Mathematical Methods in Medicine. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1132093
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1132093