International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE)

IJCRSEE Editorial Team; Gordana Calić; Branimir Radmanović; Mirjana Petrović-Lazić; Dragana Ignjatović Ristić; Nikola Subotić; Milena Mladenović

doi:10.23947/2334-8496-2025-13-2-289-310

Authors

Gordana Calić Department of Speech and Language Pathology, Faculty of Special Education and Rehabilitation, University of Belgrade, Belgrade, Serbia https://orcid.org/0000-0003-2312-1641
Branimir Radmanović Department of Psychiatry, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia; Psychiatric Clinic, University Clinical Center Kragujevac, Kragujevac, Serbia https://orcid.org/0000-0002-1690-4691
Mirjana Petrović-Lazić Department of Speech and Language Pathology, Faculty of Special Education and Rehabilitation, University of Belgrade, Belgrade, Serbia https://orcid.org/0000-0002-9496-7620
Dragana Ignjatović Ristić Department of Psychiatry, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia; Psychiatric Clinic, University Clinical Center Kragujevac, Kragujevac, Serbia https://orcid.org/0000-0002-2814-3105
Nikola Subotić Department of Psychiatry, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia; Psychiatric Clinic, University Clinical Center Kragujevac, Kragujevac, Serbia https://orcid.org/0009-0003-7517-7150
Milena Mladenović Psychiatric Clinic, University Clinical Center Kragujevac, Kragujevac, Serbia; Department of Psychology, Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia https://orcid.org/0000-0002-4973-2857

DOI:

https://doi.org/10.23947/2334-8496-2025-13-2-289-310

Keywords:

depression severity, predictors, Regression, Serbian language, acoustic analysis, perceptual analysis, Biomarker, depression recognition

Abstract

There is a growing interest in detecting depression through vocal indicators for the purpose of early diagnosis and therapeutic monitoring. Thus, research on voice characteristics in different language areas among individuals with depression may potentially contribute to the standardization of vocal analysis and the development of automatic recognition programs. This study aims to determine whether specific voice characteristics can predict the severity of depression using the Montgomery-Asberg Depression Rating Scale (MADRS) in a sample of Serbian-speaking participants. The analysis included perceptual (GRBAS scale parameters) and acoustic (parameters of frequency variability, intensity variability, and noise and tremor estimation using the MDVP software) voice characteristics in a sample of 100 participants. The sample was divided into two groups: an experimental group of participants diagnosed with depressive disorder (N = 45), including an equal number of participants with mild, moderate, and severe depression (N = 15), and a control group of participants without a depressive disorder diagnosis or depression symptoms (N = 55). The prediction of depression severity based on voice characteristics was conducted using hierarchical regression analysis. The results indicate statistically significant differences in nearly all acoustic and all perceptual voice characteristics among participants with different levels of depression symptoms (MADRS score). Post-hoc analysis revealed no differences in acoustic characteristics between subgroups with different depression severity levels. However, significant differences in perceptual characteristics were found among all subgroups, except between mild and moderate depression. After controlling for gender, age, and smoking status, depression severity demonstrated statistically significant effects on nearly all acoustic and all perceptual voice characteristics. Both perceptual and acoustic voice characteristics can predict the severity of depression. The acoustic parameter of peak amplitude variation (vAm) and the perceptual parameters of hoarseness (G), breathiness (B), asthenia (A), and strain (S) were significant predictors of depression severity. Voice may hold potential as an indicative marker in predicting the severity of depression measured by the MADRS scale. The acoustic parameter related to intensity variation and the perceptual parameters of the GRBAS scale (except voice roughness) appear to be promising voice characteristics in training depression recognition models. Identifying vocal indicators as markers for detecting mental disorders, such as depression, through regression analysis may serve as a foundation for the development of artificial intelligence models for its recognition and may have future clinical relevance.

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