Abstract: 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 depression 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.
