Background: Speech analysis has shown significant promise as a potential biomarker for depression. However, no studies to date have examined the impact of childhood trauma on speech and language patterns in individuals with depression. This study aims to explore the relationship between vocal characteristics and depressive symptoms, while also assessing how childhood trauma may shape these patterns.
Methods: 27 participants with a major depressive episode were included. The severity of depression was assessed using the Montgomery & Asberg Depression Rating Scale (MADRS) and the Beck Depression Inventory II. Childhood trauma was measured using the Childhood Trauma Questionnaire. Speech recordings from the MADRS semi-structured interview and a free clinical interview were analyzed using speaker diarization, automatic speech recognition, and feature extraction.
Results: Several acoustics features were significantly associated with depression severity. Correlation analysis revealed that greater depression severity was linked to shorter, less diverse speech, characterized by fewer words, fewer semantic clusters, and reduced articulatory effort. In contrast, childhood trauma was positively associated with distinct speech characteristics. Higher trauma load was associated with richer, longer, and more syntactically complex speech. Additionally, utterances were shorter, with more frequent shifts between semantic clusters, reflecting a more fragmented speech pattern influenced by traumatic load.
Conclusion: Our study highlights the influence of childhood trauma on vocal and linguistic characteristics of patients with depression. Automated language analysis offers the possibility to identify biomarkers of traumatic load in patients with depression. This could improve diagnostic accuracy, guide therapeutic management and monitor clinical progress.
Keywords: Language; Major depressive disorder; Precision psychiatry; Speech; Speech biomarkers; Trauma.
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