Vocal Biomarkers of Childhood Trauma: A Machine-Learning Approach to Speech Analysis

Clinical Snapshot

Key Findings

• P Value: Not reported for diagnostic accuracy measures

• Effect Size: AUC 0.89-0.996 across different classification models

• Primary Outcome: Binary classification of trauma exposure using vocal biomarkers

• Nnt Or Sensitivity: Sensitivity and specificity not reported; 68% stratified into Moderate/High Trauma Risk categories

• Confidence Interval: Not reported

Clinical Application

Requires specialized acoustic analysis software and trained personnel; may be challenging to implement in low-resource settings despite claims of scalability Limited direct applicability to Australian healthcare; may be relevant for refugee health services or multicultural communities, but would require validation in Australian populations and integration with existing trauma screening protocols Arabic-speaking children in conflict-affected regions, particularly girls aged 8-12 years

Abstract

PURPOSE: Childhood trauma can disrupt communication, yet early signs often go unrecognized in regions affected by ongoing war, where immediate physical needs take precedence. Vocal biomarkers-acoustic features linked to emotional and motor regulation-offer a promising, noninvasive means of detecting trauma-linked speech disruptions. This study applied a hybrid framework to distinguish trauma exposure in Arabic-speaking children living amid active conflict. The aim was to support scalable, speech-based tools for early trauma identification in low-resource, humanitarian settings. METHOD: We analyzed 200 publicly available recordings of spontaneous speech from Arabic-speaking girls (ages 8-12 years): 100 trauma-exposed participants from Gaza (Palestinian) and 100 non-exposed controls from Jordan. Core acoustic features (fundamental frequency [F0], jitter, shimmer, harmonics-to-noise ratio [HNR], voice onset time [VOT], first formant, second formant) informed statistical testing and theory-driven composite indices. Exploratory features-including Mel-frequency cepstral coefficients and eGeMAPSv02 descriptors-were used to train binary classification models. Three classifiers (random forest, ridge regression, and logistic regression) were evaluated using nested cross-validation and bootstrap resampling. Composite indices were combined into a 0-10 Trauma Risk Score. Generalizability was assessed using an independent Lebanese cohort (n = 80), with the trained classifier applied using fixed exploratory features and preprocessing parameters. Core features were tested post hoc for cross-cohort stability. RESULTS: Trauma-exposed children showed reduced F0 and HNR, elevated shimmer and jitter, and prolonged VOT (Cohen's d > 1.2). Binary classification models achieved strong performance (area under the curve [AUC] = .89-.92); logistic regression reached AUC = .996 under cross-validation. Composite indices (AUCs > .90) stratified 68% into Moderate/High Trauma Risk. Lebanese validation confirmed generalizability, with theory-driven features showing stable predictive patterns. CONCLUSIONS: Vocal biomarkers reliably distinguished trauma exposure in Arabic-speaking children using a simple logistic regression model. This strong performance highlights the potential of speech-based tools as scalable, noninvasive methods for early trauma detection. Further validation is needed to support their use in diverse humanitarian and conflict-affected settings.

References

1. 1.Adem, H. (2026). Vocal biomarkers of childhood trauma: A machine-learning approach to speech analysis. Journal of Speech, Language, and Hearing Research, 69(5), 1234-1248. https://doi.org/10.1044/2026_JSLHR-25-00642