Cross-Domain Transfer of Depression Voice Biomarkers Depends on the Outcome Instrument: Leakage-Controlled Cross-Sectional Evaluation Study

ArXiv [Preprint]. 2026 Sep 10:arXiv:2505.06412v3.

ABSTRACT

BACKGROUND: Voice-based depression screening is commercializing rapidly, yet whether voice biomarkers generalize across clinical settings is largely untested. Generalization is usually framed as a question about populations, but it is also a question about the outcome instrument a model is scored against: reported accuracy varies with the diagnostic criteria a study adopts, but confounded with everything else differing between studies.

OBJECTIVE: We tested whether depression voice biomarkers transfer between perinatal and general-psychiatric speech in both directions under strict leakage control, and whether transfer depends on which self-report instrument defines the outcome.

METHODS: In a cross-sectional US-nationwide online study, 446 sessions from 390 pregnant participants at 22±1 weeks' gestation (analytical sample N=316, one per participant) each provided four voice recordings, the eight-item Patient Health Questionnaire (PHQ-8), and a modified nine-item Edinburgh Postnatal Depression Scale (mEPDS-9). We extracted 375 acoustic descriptors and 330-dimensional Population Dynamics Foundation Model (PDFM) community-health embeddings. Discrimination (area under the curve, AUC) was assessed under leakage-controlled cross-validation, with feature and classifier selection inside training folds only, gated by a permutation negative-control harness, across a pre-specified grid of 4 speech tasks × 4 outcome definitions. The prenatal-trained model was applied to two general-psychiatric corpora (DAIC-WOZ, N=189; E-DAIC, N=219) under pure held-out inference, and an open-source model (DAM) trained on ~35,000 individuals was applied to all three cohorts without refitting. Multiplicity was controlled by the Benjamini-Hochberg procedure.

RESULTS: The pre-registered within-cohort outcome performed at chance (AUC 0.494, 95% CI 0.431-0.560), and no cell of the 16-cell grid survived multiplicity adjustment under either modeling paradigm. The prenatal-trained model did not transfer to either general-psychiatric corpus (AUC 0.505, 0.478). Transfer measured in the reverse direction, however, varied with the outcome instrument: DAM reached AUC 0.706-0.708 on general-psychiatric speech, 0.510 (0.411-0.609) against the PHQ-8 in pregnant women, and 0.645 (0.541-0.744) against the mEPDS-9 in the same women; paired difference 0.135 (0.019-0.248; unadjusted post-hoc p=0.021). An exploratory item-level analysis suggests a mechanism: DAM tracked affective-distress items (crying r=0.26, sadness r=0.21, overwhelmed r=0.20) but not the somatic PHQ-8 items, though only crying survived adjustment across 17 tests. Transfer also varied descriptively by elicitation task (pregnancy narrative 0.653, 95% CI 0.550-0.753; open-ended speech 0.597, 0.486-0.699; scripted reading 0.544, 0.434-0.651), not compared formally.

CONCLUSIONS: Whether a depression voice biomarker transfers may depend not only on the population but on whether the outcome instrument captures what the model can detect: in the same pregnant participants, a general-population speech model discriminated better against the mEPDS-9 than against the PHQ-8. Validation of voice-based mental-health tools should state the population, the elicitation task, and the outcome instrument together, and a tool validated against one instrument should not be assumed to work against another.

TRIAL REGISTRATION: Preregistered on the Open Science Framework.[1].

PMID:42818543 | PMC:PMC13622871