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

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.