Detecting Depression from Speech through an Attentive LSTM Network

Zhao, Yan, Xie, Yue, Liang, Ruiyu, Zhang, Li, Zhao, Li and Liu, Chengyu (2021) Detecting Depression from Speech through an Attentive LSTM Network. IEICE Transactions on Information and Systems, E104.D (11). pp. 2019-2023. ISSN 0916-8532

E104.D_2020EDL8132.pdf - Published Version

Download (258kB) | Preview
Official URL:


Depression endangers people's health conditions and affects the social order as a mental disorder. As an efficient diagnosis of depression, automatic depression detection has attracted lots of researcher's interest. This study presents an attention-based Long Short-Term Memory (LSTM) model for depression detection to make full use of the difference between depression and non-depression between timeframes. The proposed model uses frame-level features, which capture the temporal information of depressive speech, to replace traditional statistical features as an input of the LSTM layers. To achieve more multi-dimensional deep feature representations, the LSTM output is then passed on attention layers on both time and feature dimensions. Then, we concat the output of the attention layers and put the fused feature representation into the fully connected layer. At last, the fully connected layer's output is passed on to softmax layer. Experiments conducted on the DAIC-WOZ database demonstrate that the proposed attentive LSTM model achieves an average accuracy rate of 90.2% and outperforms the traditional LSTM network and LSTM with local attention by 0.7% and 2.3%, respectively, which indicates its feasibility.

Item Type: Article
Uncontrolled Keywords: Artificial Intelligence, Electrical and Electronic Engineering, Computer Vision and Pattern Recognition, Hardware and Architecture, Software
Subjects: G600 Software Engineering
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 15 Nov 2021 14:19
Last Modified: 15 Nov 2021 14:30

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics