Research Area:  Machine Learning

Abstract:

Depression is a global mental health problem, the worst case of which can lead to suicide. An automatic depression detection system provides great help in facilitating depression self-assessment and improving diagnostic accuracy. In this work, we propose a novel depression detection approach utilizing speech characteristics and linguistic contents from participants interviews. In addition, we establish an Emotional Audio-Textual Depression Corpus (EATD-Corpus) which contains audios and extracted transcripts of responses from depressed and non-depressed volunteers. To the best of our knowledge, EATD-Corpus is the first and only public depression dataset that contains audio and text data in Chinese. Evaluated on two depression datasets, the proposed method achieves the state-of-the-art performances. The outperforming results demonstrate the effectiveness and generalization ability of the proposed method.

Keywords:  
Automatic Depression Detection
Emotional Audio-Textual Corpus
Gru-Bilstm
mental health problem
Deep Learning

Author(s) Name:  Ying Shen; Huiyu Yang; Lin Lin

Publisher name:  IEEE

DOI:  10.1109/ICASSP43922.2022.9746569

Volume Information:  

Paper Link:   https://ieeexplore.ieee.org/abstract/document/9746569