ArticleBMJ mental health2025
Acoustic signatures of depression elicited by emotion-based and theme-based speech tasks.
Article in BMJ mental health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundMajor depressive disorder (MDD) remains underdiagnosed worldwide, partly due to reliance on self-reported symptoms and clinician-administered interviews.
objectiveThis 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.
methodsA 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 Additive exPlanations (SHAP) applied for feature interpretation. Four traditional machine learning models (support vector machine, decision tree, k-nearest neighbour, random forest) served as comparators. Within-subject variation in speech duration was assessed with repeated-measures Analysis of Variance.
findingsThe LSTM model outperformed traditional classifiers, capturing temporal and dynamic speech patterns. The positive-valence image task achieved the highest accuracy (87.5%), followed by the negative-valence (85.0%), TAT (84.2%) and neutral-valence (81.7%) tasks. SHAP analysis highlighted task-specific contributions of pitch-related and spectral features. Significant differences in speech duration across tasks (p<0.01) indicated that affective valence influenced speech production.
conclusionsEmotionally enriched and thematically ambiguous tasks enhanced automated MDD detection, with positive-valence stimuli providing the greatest discriminative power. SHAP interpretation underscored the importance of tailoring models to different speech inputs. CLINICAL IMPLICATIONS: Speech-based models incorporating emotionally evocative and projective stimuli offer a scalable, non-invasive approach for early depression screening. Their reliance on natural speech supports cross-cultural application and reduces stigma and literacy barriers. Broader validation is needed to facilitate integration into routine screening and monitoring.
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