IMR Press / FBL / Volume 26 / Issue 12 / DOI: 10.52586/5066
Open Access Rapid Report
Speech depression recognition based on attentional residual network
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1 Internet Education Data Learning Analysis Technology National and Local Joint Engineering Laboratory, 730070 Lanzhou, Gansu, China
2 Key Laboratory of Behavior and Mental Health of Gansu Province, 730070 Lanzhou, Gansu, China
3 School of Physics and Electronic Engineering, Northwest Normal University, 730070 Lanzhou, Gansu, China

Academic Editor: Dimitria Electra Gatzia

Front. Biosci. (Landmark Ed) 2021, 26(12), 1746–1759; https://doi.org/10.52586/5066
Submitted: 21 April 2021 | Revised: 19 August 2021 | Accepted: 9 October 2021 | Published: 30 December 2021
(This article belongs to the Special Issue Synesthesia, hallucination and mental disorders)
Copyright: © 2021 The Author(s). Published by BRI.
This is an open access article under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).
Abstract

Background: Depressive disorder is a common affective disorder, also known as depression, which is characterized by sadness, loss of interest, feelings of guilt or low self-worth and poor concentration. As speech is easy to obtain non-offensively with low-cost, many researchers explore the possibility of depression prediction through speech. Adopting speech signals to recognize depression has important practical significance. Aiming at the problem of the complex structure of the deep neural network method used in the recognition of speech depression and the traditional machine learning methods need to manually extract the features and the low recognition rate. Methods: This paper proposes a model that combines residual thinking and attention mechanism. First, depression corpus is designed based on the classic psychological experimental paradigm self-reference effect (SRE), and the speech dataset is labeled; then the attention module is introduced into the residual, and the channel attention is used to learn the features of the channel dimension, the spatial attention feedback the features of the spatial dimension, and the combination of the two to obtain the attention residual unit; finally the stacking unit constructs a speech depression recognition model based on the attention residual network. Results: Experimental results show that compared with traditional machine learning methods, this model obtains better results in the recognition of depression, which can meet the need for actual recognition application of depression. Conclusions: In this study, we not only predict whether person is depressed, but also estimate the severity of depression. In the designed corpus, the depression binary classification of an individual is given based on the severity of depression which is measured using BDI-II scores. Experimental results show that spontaneous speech can obtain better results than automatic speech, and the classification of speech features corresponding to negative questions is better than other tasks under negative emotions. Besides, the recognition accuracy rate of both male and female subjects is higher than that under other emotions.

Keywords
Automatic recognition of depression
Residual neural network
Attention mechanism
MFCC
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