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Related Experiment Video

Updated: Jul 3, 2026

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Published on: July 7, 2023

Depression markers in speech: An approach based on tract variables dynamics.

Sahar Altalhi^1,2, Tanaya Guha¹, Alessandro Vinciarelli¹

¹University of Glasgow, Glasgow, United Kingdom.

The Journal of the Acoustical Society of America

|July 2, 2026

Summary

This study introduces novel depression biomarkers using speech dynamics, analyzing articulatory predictability, complexity, and randomness. These biomarkers effectively distinguish individuals with depression from controls in read and spontaneous speech.

Related Experiment Videos

Last Updated: Jul 3, 2026

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder

Published on: July 7, 2023

Area of Science:

Speech Science
Biomarkers
Clinical Psychology

Background:

Depression diagnosis often relies on subjective assessments.
Objective biomarkers for depression are needed to improve diagnostic accuracy.
Previous research has not fully explored articulatory dynamics for depression detection.

Purpose of the Study:

To identify novel depression biomarkers from speech.
To quantify articulatory process aspects like predictability, complexity, and randomness.
To evaluate the efficacy of these biomarkers in discriminating depressed individuals.

Main Methods:

Utilized dynamical properties of tract variables as speech biomarkers.
Quantified predictability using Largest Lyapunov Exponent.
Measured complexity and randomness with Correlation Dimension and Sample Entropy, respectively.
Conducted experiments on the Androids Corpus dataset.

Main Results:

The proposed biomarkers demonstrated high effectiveness in discriminating between depressed and control speakers.
Significant differences were observed in articulatory dynamics between groups.
High Cliff's delta values confirmed biomarker efficacy in both read and spontaneous speech.

Conclusions:

Dynamical properties of speech articulators serve as effective depression biomarkers.
This approach offers a novel, objective method for depression assessment.
Further research can explore these biomarkers for early detection and monitoring of depression.