Exploring the ability of vocal biomarkers in distinguishing depression from bipolar disorder, schizophrenia, and healthy controls

Wei Pan; Fusong Deng; Xianbin Wang; Bowen Hang; Wenwei Zhou; Tingshao Zhu

doi:10.3389/fpsyt.2023.1079448

Exploring the ability of vocal biomarkers in distinguishing depression from bipolar disorder, schizophrenia, and healthy controls

Front Psychiatry. 2023 Jul 20:14:1079448. doi: 10.3389/fpsyt.2023.1079448. eCollection 2023.

Authors

Wei Pan^{1

2

3}, Fusong Deng⁴, Xianbin Wang^{1

2

3}, Bowen Hang^{1

2

3}, Wenwei Zhou^{1

2

3}, Tingshao Zhu^{5

6}

Affiliations

¹ Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan, China.
² School of Psychology, Central China Normal University, Wuhan, China.
³ Key Laboratory of Human Development and Mental Health of Hubei Province, Wuhan, China.
⁴ Wuhan Wuchang Hospital, Wuchang Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China.
⁵ Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
⁶ CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.

Abstract

Background: Vocal features have been exploited to distinguish depression from healthy controls. While there have been some claims for success, the degree to which changes in vocal features are specific to depression has not been systematically studied. Hence, we examined the performances of vocal features in differentiating depression from bipolar disorder (BD), schizophrenia and healthy controls, as well as pairwise classifications for the three disorders.

Methods: We sampled 32 bipolar disorder patients, 106 depression patients, 114 healthy controls, and 20 schizophrenia patients. We extracted i-vectors from Mel-frequency cepstrum coefficients (MFCCs), and built logistic regression models with ridge regularization and 5-fold cross-validation on the training set, then applied models to the test set. There were seven classification tasks: any disorder versus healthy controls; depression versus healthy controls; BD versus healthy controls; schizophrenia versus healthy controls; depression versus BD; depression versus schizophrenia; BD versus schizophrenia.

Results: The area under curve (AUC) score for classifying depression and bipolar disorder was 0.5 (F-score = 0.44). For other comparisons, the AUC scores ranged from 0.75 to 0.92, and the F-scores ranged from 0.73 to 0.91. The model performance (AUC) of classifying depression and bipolar disorder was significantly worse than that of classifying bipolar disorder and schizophrenia (corrected p < 0.05). While there were no significant differences in the remaining pairwise comparisons of the 7 classification tasks.

Conclusion: Vocal features showed discriminatory potential in classifying depression and the healthy controls, as well as between depression and other mental disorders. Future research should systematically examine the mechanisms of voice features in distinguishing depression with other mental disorders and develop more sophisticated machine learning models so that voice can assist clinical diagnosis better.

Keywords: bipolar disorder; depression; healthy controls; i-vectors; logistic regression MFCCs; schizophrenia.