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

Abstract

Whether voice biomarkers of depression generalize across clinical settings is largely untested. Generalization is usually framed as a question about populations. It is also a question about the outcome instrument a model is scored against, a dimension confounded in existing studies with all else that differs between them. In a US-nationwide online study, 446 sessions from 390 pregnant participants at 22 weeks' gestation (analytical N=316) each gave four voice recordings, the PHQ-8 and a modified 9-item EPDS (mEPDS-9). Discrimination 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 4 task x 4 outcome grid with Benjamini-Hochberg adjustment. The model was applied to DAIC-WOZ (N=189) and E-DAIC (N=219) under held-out inference; an open-source model trained on ~35,000 individuals was applied to all three cohorts without refitting. The pre-registered within-cohort outcome was at chance (AUC 0.494, 95% CI 0.431-0.560) and no grid cell survived adjustment under either modeling paradigm. The prenatal-trained model did not transfer (0.505, 0.478). Transfer in the reverse direction varied with the outcome instrument: the general-population model reached 0.706-0.708 on general-psychiatric speech, 0.510 (0.411-0.609) against the PHQ-8, and 0.645 (0.541-0.744) against the mEPDS-9 in the same pregnant participants; paired difference 0.135 (0.019-0.248), unadjusted post-hoc p=0.021. Item-level analyses suggest an explanation, though only 1 of 17 tests survived adjustment. The mEPDS-9 used a generic response scale, not the published EPDS anchors, so its thresholds are operational, not validated. Validation should specify population, task and instrument together.