Depression Severity Detection Using Read Speech with a Divide-and-Conquer Approach

被引：7

作者：

Kwon, Namhee ^{[1
]}

Kim, Samuel ^{[1
]}

机构：

[1] Canary Speech LLC, Provo, UT 84604 USA

来源：

2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) | 2021年

关键词：

SCALE;

D O I：

10.1109/EMBC46164.2021.9629868

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

We propose a divide-and-conquer approach to detect depression severity using speech. We divide speech features based on their attributes, i.e., acoustic, prosodic, and language features, then fuse them in a modeling stage with fully connected deep neural networks. Experiments with 76 clinically depressed patients (38 severe and 38 moderate in terms of Montgomery-Asberg Depression Rating Scale (MADRS)), we obtain 78% accuracy while patients' self-reporting scores can classify their own status with 79% accuracy.

引用

页码：633 / 637

页数：5

共 27 条

[1]

Afshan Amber, 2018, INTERSPEECH

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