Automated speech-based screening of depression using deep convolutional neural networks
Abstract
Early detection and treatment of depression is essential in promoting remission, preventing relapse, and reducing the emotional burden of the disease. Current diagnoses are primarily subjective, inconsistent across professionals, and expensive for individuals who may be in urgent need of help. This paper proposes a novel approach to automated depression detection in speech using convolutional neural network (CNN) and multipart interactive training. The model was tested using 2568 voice samples obtained from 77 non-depressed and 30 depressed individuals. In experiment conducted, data were applied to residual CNNs in the form of spectrograms, images auto-generated from audio samples. The experimental results obtained using different ResNet architectures gave a promising baseline accuracy reaching 77%.
Cite
@article{arxiv.1912.01115,
title = {Automated speech-based screening of depression using deep convolutional neural networks},
author = {Karol Chlasta and Krzysztof Wołk and Izabela Krejtz},
journal= {arXiv preprint arXiv:1912.01115},
year = {2020}
}
Comments
10 pages, 8 figures and 2 tables, HCist 2019 - 8th International Conference on Health and Social Care Information Systems and Technologies (16-18 October 2019, Sousse, Tunisia)