Reading between the Lines: Leveraging Large Language Models for Global Dementia and Depression Assessment from Clinical Interviews
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Electrical Engineering and Systems Science > Audio and Speech Processing
arXiv:2606.18019 (eess)
[Submitted on 16 Jun 2026]
Title:Reading between the Lines: Leveraging Large Language Models for Global Dementia and Depression Assessment from Clinical Interviews
Authors:Franziska Braun, Alea Rüggeberg, Thomas Ranzenberger, Hartmut Lehfeld, Thomas Hillemacher, Tobias Bocklet, Korbinian Riedhammer
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Abstract:Dementia and depression are the most prevalent neuropsychiatric disorders in geriatric populations, and their overlapping symptoms pose major challenges for differential diagnosis. In this study, we investigate open-weights Large Language Models (LLMs) for predicting dementia and depression severity from speech samples collected during standardized history taking interviews with 154 German-speaking subjects. We introduce an observer-based Global Depression Scale (GDS-D) aligned with the established Global Deterioration Scale (GDS), enabling parallel global staging of affective and cognitive symptoms. We compare three LLMs (Mistral 3.1, DeepHermes, Qwen3) in two settings: (1) zero-shot prediction and (2) LLM-based feature extraction for Support Vector Regression, using human and pause-enriched transcripts. Results show that LLMs effectively predict depression severity in zero-shot settings (best MAE of 0.60), while dementia assessment benefits substantially from structured feature extraction (best MAE of 0.78), reducing errors by up to 35% over zero-shot baselines. Pause-enriched transcripts achieve competitive performance with human transcriptions, demonstrating the viability of fully automatic screening pipelines for differential neuropsychiatric assessment.
| Comments: | Accepted for publication in Text, Speech and Dialogue (TSD 2026). The final authenticated publication will be available online via Springer LNCS/LNAI |
| Subjects: | Audio and Speech Processing (eess.AS); Computation and Language (cs.CL); Sound (cs.SD) |
| Cite as: | arXiv:2606.18019 [eess.AS] |
| (or arXiv:2606.18019v1 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2606.18019
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Reading between the Lines: Leveraging Large Language Models for Global Dementia and Depression Assessment from Clinical Interviews, by Franziska Braun and Alea R\"uggeberg and Thomas Ranzenberger and Hartmut Lehfeld and Thomas Hillemacher and Tobias Bocklet and Korbinian Riedhammer
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