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Detection of Minor and Major Depression through Voice as a Biomarker Using Machine Learning.

Daun Shin1,2, Won Ik Cho3, C Hyung Keun Park4

  • 1Department of Psychiatry, Seoul National University College of Medicine, Seoul 03080, Korea.

Journal of Clinical Medicine
|July 24, 2021
PubMed
Summary

Voice analysis may help detect depression. This study found distinct voice features differentiate between individuals with no depression, minor depression, and major depression, suggesting voice as a potential biomarker.

Keywords:
dimensional approachmachine learningmajor depressive episodeminor depressive episodevoice

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Area of Science:

  • Psychiatry and Psychology
  • Biomedical Engineering
  • Speech and Audio Signal Processing

Background:

  • Depression, both minor and major, is a significant global health issue with substantial social burden.
  • Currently, objective biomarkers for detecting minor depression are lacking, hindering timely diagnosis and intervention.
  • Voice analysis presents a potential non-invasive method for identifying physiological and psychological changes associated with depression.

Purpose of the Study:

  • To investigate the potential of voice as a biomarker for distinguishing between individuals with no depression, minor depressive episodes, and major depressive episodes.
  • To identify specific voice features that differ across these three depressive states.
  • To evaluate the efficacy of machine learning models in classifying depressive states based on voice characteristics.

Main Methods:

  • Ninety-three participants were categorized into three groups: not depressed (n=33), minor depressive episode (n=26), and major depressive episode (n=34).
  • Twenty-one acoustic voice features were extracted from semi-structured interview recordings.
  • Analysis of variance (ANOVA) was used for group comparisons, and machine learning models, including multi-layer processing, were applied for classification.

Main Results:

  • Seven voice indicators demonstrated significant differences between the three groups, even after adjusting for covariates like age, BMI, and non-psychiatric medications.
  • The multi-layer processing machine learning model achieved an Area Under the Curve (AUC) of 65.9%, with 65.6% sensitivity and 66.2% specificity.
  • Machine learning accurately distinguished between the not depressed, minor depression, and major depression groups based on voice features.

Conclusions:

  • Voice characteristics exhibit differences related to depressive episodes, supporting their use as potential biomarkers.
  • This study is the first to explore voice changes specifically in minor depression, highlighting its potential for early detection.
  • Despite a small sample size, the findings suggest that voice analysis, particularly with machine learning, can differentiate depressive states, paving the way for objective diagnostic tools.