Fast and accurate assessment of depression based on voice acoustic features: a cross-sectional and longitudinal study

Yang Wang; Lijuan Liang; Zhongguo Zhang; Xiao Xu; Rongxun Liu; Hanzheng Fang; Ran Zhang; Yange Wei; Zhongchun Liu; Rongxin Zhu; Xizhe Zhang; Fei Wang

doi:10.3389/fpsyt.2023.1195276

Fast and accurate assessment of depression based on voice acoustic features: a cross-sectional and longitudinal study

Front Psychiatry. 2023 Jun 21:14:1195276. doi: 10.3389/fpsyt.2023.1195276. eCollection 2023.

Authors

Yang Wang^{1

2

3}, Lijuan Liang^{2

3

4}, Zhongguo Zhang^{2

3

5}, Xiao Xu⁶, Rongxun Liu^{2

3

7}, Hanzheng Fang⁸, Ran Zhang^{2

3}, Yange Wei^{2

3}, Zhongchun Liu⁹, Rongxin Zhu^{2

3}, Xizhe Zhang⁶, Fei Wang^{2

3}

Affiliations

¹ Psychology Institute, Inner Mongolia Normal University, Hohhot, Inner Mongolia, China.
² Early Intervention Unit, Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.
³ Functional Brain Imaging Institute, Nanjing Medical University, Nanjing, China.
⁴ Laboratory of Psychology, The First Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China.
⁵ The Fourth People's Hospital of Yancheng, Yancheng, Jiangsu, China.
⁶ School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
⁷ College of Medical Engineering, Xinxiang Medical University, Xinxiang, Henan, China.
⁸ School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China.
⁹ Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.

Abstract

Background: Depression is a widespread mental disorder that affects a significant portion of the population. However, the assessment of depression is often subjective, relying on standard questions or interviews. Acoustic features have been suggested as a reliable and objective alternative for depression assessment. Therefore, in this study, we aim to identify and explore voice acoustic features that can effectively and rapidly predict the severity of depression, as well as investigate the potential correlation between specific treatment options and voice acoustic features.

Methods: We utilized voice acoustic features correlated with depression scores to train a prediction model based on artificial neural network. Leave-one-out cross-validation was performed to evaluate the performance of the model. We also conducted a longitudinal study to analyze the correlation between the improvement of depression and changes in voice acoustic features after an Internet-based cognitive-behavioral therapy (ICBT) program consisting of 12 sessions.

Results: Our study showed that the neural network model trained based on the 30 voice acoustic features significantly correlated with HAMD scores can accurately predict the severity of depression with an absolute mean error of 3.137 and a correlation coefficient of 0.684. Furthermore, four out of the 30 features significantly decreased after ICBT, indicating their potential correlation with specific treatment options and significant improvement in depression (p < 0.05).

Conclusion: Voice acoustic features can effectively and rapidly predict the severity of depression, providing a low-cost and efficient method for screening patients with depression on a large scale. Our study also identified potential acoustic features that may be significantly related to specific treatment options for depression.

Keywords: Internet-based cognitive-behavioral therapy; cross-sectional; deep learning; depression; longitudinal; voice acoustic features.