Acoustic signatures of depression elicited by emotion-based and theme-based speech tasks.

ABSTRACT:

Background

Major depressive disorder (MDD) remains underdiagnosed worldwide, partly due to reliance on self-reported symptoms and clinician-administered interviews.

Objective

This study examined whether a speech-based classification model using emotionally and thematically varied image-description tasks could effectively distinguish individuals with MDD from healthy controls.

Methods

A total of 120 participants (59 with MDD, 61 healthy controls) completed four speech tasks: three emotionally valenced images (positive, neutral, negative) and one Thematic Apperception Test (TAT) stimulus. Speech responses were segmented, and 23 acoustic features were extracted per sample. Classification was performed using a long short-term memory (LSTM) neural network, with SHapley Add

SUBMITTER: Lin Q 

PROVIDER: S-EPMC12481279 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature