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Multi-head attention-based long short-term memory for depression detection from speech.

Zhao, Yan; Liang, Zhenlin; Du, Jing; Zhang, Li; Liu, Chengyu; Zhao, Li


Yan Zhao

Zhenlin Liang

Jing Du

Li Zhang

Chengyu Liu

Li Zhao


Depression is a mental disorder that threatens the health and normal life of people. Hence, it is essential to provide an effective way to detect depression. However, research on depression detection mainly focuses on utilizing different parallel features from audio, video, and text for performance enhancement regardless of making full usage of the inherent information from speech. To focus on more emotionally salient regions of depression speech, in this research, we propose a multi-head time-dimension attention-based long short-term memory (LSTM) model. We first extract frame-level features to store the original temporal relationship of a speech sequence and then analyze their difference between speeches of depression and those of health status. Then, we study the performance of various features and use a modified feature set as the input of the LSTM layer. Instead of using the output of the traditional LSTM, multi-head time-dimension attention is employed to obtain more key time information related to depression detection by projecting the output into different subspaces. The experimental results show the proposed model leads to improvements of 2.3 and 10.3% over the LSTM model on the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) and the Multi-modal Open Dataset for Mental-disorder Analysis (MODMA) corpus, respectively.


ZHAO, Y., LIANG, Z., DU, J., ZHANG, L., LIU, C. and ZHAO, L. 2021. Multi-head attention-based long short-term memory for depression detection from speech. Frontiers in neurorobotics [online], 15, article 684037. Available from:

Journal Article Type Article
Acceptance Date Jul 19, 2021
Online Publication Date Aug 26, 2021
Publication Date Dec 31, 2021
Deposit Date Sep 21, 2021
Publicly Available Date Sep 21, 2021
Journal Frontiers in Neurorobotics
Electronic ISSN 1662-5218
Publisher Frontiers Media
Peer Reviewed Peer Reviewed
Volume 15
Article Number 684037
Keywords Depression; LSTM; Multi-head attention; Frame-level feature; Deep learning
Public URL


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