Cross-Corpus Depression Detection in Semi-Structured Clinical Interviews
MCML Authors
Andreas Triantafyllopoulos
Dr.
Mahdis Habibpour
Robert Richer
Dr.
Björn Schuller
Prof. Dr.
Core PI
Björn Eskofier
Prof. Dr.
Core PI
Abstract
Andreas Triantafyllopoulos
Dr.
Mahdis Habibpour
Robert Richer
Dr.
Björn Schuller
Prof. Dr.
Core PI
Björn Eskofier
Prof. Dr.
Core PI
Abstract
Previous work links Major Depressive Disorder (MDD) to linguistic and paralinguistic speech patterns, highlighting speech analysis as a potential diagnostic tool for remote monitoring or clinical decision support (CDS) systems. Recent attempts to build MDD diagnostic models primarily rely on the English (E-)DAIC corpus. Despite promising results, these works rarely evaluate cross-corpus generalization and rely on self-reported questionnaires, limiting their clinical utility. We introduce a new, clinically validated German corpus, EmpkinS-EKSpression, of semi-structured diagnostic interviews to test the reliability of spoken language-based measures of MDD. Our model achieves an F1-score of 0.86 on the EmpkinS-EKSpression dataset and 0.74 on E-DAIC via zero-shot cross-corpus transfer, suggesting that MDD detection models can generalize across corpora and languages despite differences in interview format and ground truth definition, supporting further development of CDS systems.
inproceedings SNT+26
EMNLP 2026
Conference on Empirical Methods in Natural Language Processing. Budapest, Hungary, Oct 24-29, 2026. To be published.
Authors
M. Sadeghi • P. T. L. Nguyen • A. Triantafyllopoulos • M. Habibpour • R. Richer • L. H. Rupp • L. Schindler-Gmelch • M. Keinert • M. Hager • B. W. Schuller • B. Egger • M. Berking • B. M. EskofierResearch Areas
BibTeXKey: SNT+26