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Automatic Depression Detection: an Emotional Audio-Textual Corpus and A Gru-Bilstm-Based Model - 2022

Automatic Depression Detection: An Emotional Audio-Textual Corpus And A Gru-Bilstm-Based Model

Research Paper on Automatic Depression Detection: An Emotional Audio-Textual Corpus And A Gru-Bilstm-Based Model

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

Journal name:  

Conferrence name:  ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Publisher name:  IEEE

DOI:  10.1109/ICASSP43922.2022.9746569

Volume Information: