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  1. Home
  3. [automated Audio Analysis And Depression: A Systematic Umbrella Review].
  1. Home
  3. [automated Audio Analysis And Depression: A Systematic Umbrella Review].

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[Automated audio analysis and depression: A systematic umbrella review].

Bálint Hajduska-Dér1, Lajos Simon1, János Réthelyi1

  • 1Semmelweis Egyetem, Pszichiátriai és Pszichoterápiás Klinika, Budapest.

Ideggyogyaszati Szemle
|March 31, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning and voice analysis offer objective depression diagnosis, overcoming limitations of traditional methods. Further research and diverse validation are needed for clinical application.

Keywords:
automated voice analysisdepressionmachine learning

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Area of Science:

  • Psychiatry and Mental Health
  • Computational Linguistics
  • Biomedical Engineering

Background:

  • Traditional depression diagnosis is subjective and time-consuming.
  • Automated voice analysis provides objective, biometric measurements.
  • Machine learning (ML) enhances voice analysis for depression detection.

Purpose of the Study:

  • To review ML-supported voice analysis research for depression.
  • Identify inconsistencies in current research practices and findings.
  • Provide recommendations for future research directions.

Main Methods:

  • Umbrella review methodology integrating systematic literature reviews and meta-analyses.
  • Searched PubMed, Scopus, and ProQuest databases adhering to PRISMA guidelines (last 5 years).
  • Assessed methodological quality of selected publications using AMSTAR2.

Main Results:

  • 162 unique records identified; 6 publications selected for detailed analysis.
  • Identified factors limiting model applicability and highlighted acoustic biomarkers for depression.
  • Confirmed the value of ML and voice analysis in advancing depression diagnostics.

Conclusions:

  • ML-powered voice analysis offers objective and early depression detection.
  • Potential for cost-effective mental healthcare and improved access.
  • Further research, standardization, and diverse validation are crucial for clinical translation.