Detecting Depression from Speech through an Attentive LSTM Network

Yan ZHAO  Yue XIE  Ruiyu LIANG  Li ZHANG  Li ZHAO  Chengyu LIU  

IEICE TRANSACTIONS on Information and Systems   Vol.E104-D    No.11    pp.2019-2023
Publication Date: 2021/11/01
Publicized: 2021/08/24
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2020EDL8132
Type of Manuscript: LETTER
Category: Speech and Hearing
depression detection,  LSTM,  attention mechanism,  vocal expression,  

Full Text: PDF(252.2KB)>>
Buy this Article

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.

open access publishing via