A fast-growing area of mental health research is the search for speech-based objective markers for conditions such as depression. One vital challenge in the development of speech-based depression severity assessment systems is the extraction of depression-relevant features from speech signals. In order to deliver more comprehensive feature representation, we herein explore the benefits of a hybrid network that encodes depression-related characteristics in speech for the task of depression severity assessment. The proposed network leverages self-attention networks (SAN) trained on low-level acoustic features and deep convolutional neural networks (DCNN) trained on 3D Log-Mel spectrograms. The feature representations learnt in the SAN and DCNN are concatenated and average pooling is exploited to aggregate complementary segment-level features. Finally, support vector regression is applied to predict a speaker’s Beck Depression Inventory-II score. Experiments based on a subset of the Audio-Visual Depressive Language Corpus, as used in the 2013 and 2014 Audio/Visual Emotion Challenges, demonstrate the effectiveness of our proposed hybrid approach.
Cite as: Zhao, Z., Li, Q., Cummins, N., Liu, B., Wang, H., Tao, J., Schuller, B.W. (2020) Hybrid Network Feature Extraction for Depression Assessment from Speech. Proc. Interspeech 2020, 4956-4960, doi: 10.21437/Interspeech.2020-2396
@inproceedings{zhao20h_interspeech,
author={Ziping Zhao and Qifei Li and Nicholas Cummins and Bin Liu and Haishuai Wang and Jianhua Tao and Björn W. Schuller},
title={{Hybrid Network Feature Extraction for Depression Assessment from Speech}},
year=2020,
booktitle={Proc. Interspeech 2020},
pages={4956--4960},
doi={10.21437/Interspeech.2020-2396}
}