Detecting Depression Using an Ensemble Logistic Regression Model Based on Multiple Speech Features

Haihua Jiang, Bin Hu*, Zhenyu Liu, Gang Wang, Lan Zhang, Xiaoyu Li, Huanyu Kang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

71 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number6508319
JournalComputational and Mathematical Methods in Medicine
Volume2018
DOIs
Publication statusPublished - 2018
Externally publishedYes

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