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

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

来源：Computational and Mathematical Methods in Medicine, 2018, 2018: 6508319.

DOI：10.1155/2018/6508319

• CLINICAL DEPRESSION
• ACOUSTIC MEASURES
• MAJOR DEPRESSION
• CLASSIFICATION
• SEVERITY
• PREDICTION
• SUICIDE

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.

• 出版日期2018
• 单位北京工业大学; 兰州大学; 首都医科大学