Reading between the Lines: Leveraging Large Language Models for Global Dementia and Depression Assessment from Clinical Interviews
Mirrored from arXiv — NLP / Computation & Language for archival readability. Support the source by reading on the original site.
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
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
View PDF
HTML (experimental)
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)
|
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
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
Current browse context:
eess.AS
References & Citations
Loading...
Bookmark
Bibliographic Tools
Code, Data, Media
Demos
Related Papers
About arXivLabs
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — NLP / Computation & Language
-
Generating in the Limit with Infinitely Many Hallucinations
Jun 30
-
Extracting Knowledge from an Arabic-English Machine-Readable Dictionary Using Information Extraction
Jun 30
-
Developmental Trajectories of Situation Modeling and Mentalizing in Transformer Language Models
Jun 30
-
A French OSCE Dialogue Dataset and Controllable Virtual Patient System for Clinical Training
Jun 30
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.