@inproceedings{sadeghi-etal-2026-cross,
title = "Cross-Corpus Depression Detection in Semi-Structured Clinical Interviews",
author = {Sadeghi, Misha and
Nguyen, Phuc Truong Loc and
Triantafyllopoulos, Andreas and
Habibpour, Mahdis and
Richer, Robert and
Rupp, Lydia Helene and
Schindler-Gmelch, Lena and
Keinert, Marie and
Hager, Malin and
Schuller, Bj{\"o}rn and
Egger, Bernhard and
Berking, Matthias and
Eskofier, Bjoern},
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2663/",
pages = "60628--60646",
ISBN = "979-8-89176-459-0",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Cross-Corpus Depression Detection in Semi-Structured Clinical Interviews
%A Sadeghi, Misha
%A Nguyen, Phuc Truong Loc
%A Triantafyllopoulos, Andreas
%A Habibpour, Mahdis
%A Richer, Robert
%A Rupp, Lydia Helene
%A Schindler-Gmelch, Lena
%A Keinert, Marie
%A Hager, Malin
%A Schuller, Björn
%A Egger, Bernhard
%A Berking, Matthias
%A Eskofier, Bjoern
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F sadeghi-etal-2026-cross
%X 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.
%U https://aclanthology.org/2026.emnlp-main.2663/
%P 60628-60646
Markdown (Informal)
[Cross-Corpus Depression Detection in Semi-Structured Clinical Interviews](https://aclanthology.org/2026.emnlp-main.2663/) (Sadeghi et al., EMNLP 2026)
ACL
- Misha Sadeghi, Phuc Truong Loc Nguyen, Andreas Triantafyllopoulos, Mahdis Habibpour, Robert Richer, Lydia Helene Rupp, Lena Schindler-Gmelch, Marie Keinert, Malin Hager, Björn Schuller, Bernhard Egger, Matthias Berking, and Bjoern Eskofier. 2026. Cross-Corpus Depression Detection in Semi-Structured Clinical Interviews. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 60628–60646, Budapest, Hungary. Association for Computational Linguistics.