Kwon Namhee, Kim Samuel
Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:633-637. doi: 10.1109/EMBC46164.2021.9629868.
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.
我们提出一种分而治之的方法，利用语音来检测抑郁严重程度。我们根据语音特征的属性，即声学、韵律和语言特征，对语音特征进行划分，然后在建模阶段使用全连接深度神经网络将它们融合起来。对76名临床抑郁症患者（根据蒙哥马利-阿斯伯格抑郁评定量表（MADRS），38名重度患者和38名中度患者）进行的实验中，我们获得了78%的准确率，而患者的自我报告分数对其自身状态进行分类的准确率为79%。
