Infusing Acoustic Pause Context into Text-Based Dementia Assessment

  • Speech pauses, alongside content and structure, offer a valuable and non-invasive biomarker for detecting dementia. This work investigates the use of pause-enriched transcripts in transformer-based language models to differentiate the cognitive states of subjects with no cognitive impairment, mild cognitive impairment, and Alzheimer’s dementia based on their speech from a clinical assessment. We address three binary classification tasks: Onset, monitoring, and dementia exclusion. The performance is evaluated through experiments on a German Verbal Fluency Test and a Picture Description Test, comparing the model’s effectiveness across different speech production contexts. Starting from a textual baseline, we investigate the effect of incorporation of pause information and acoustic context. We show the test should be chosen depending on the task, and similarly, lexical pause information and acoustic cross-attention contribute differently.

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Author:Franziska Braun, Sebastian Bayerl, Florian Hönig, Hartmut Lehfeld, Thomas Hillemacher, Tobias BockletORCiD, Korbinian RiedhammerORCiD
DOI:https://doi.org/10.21437/Interspeech.2024-2496
ISSN:2958-1796
Document Type:conference proceeding (article)
Language:English
Date of first Publication:2024/06/14
Reviewed:Begutachtet/Reviewed
Release Date:2025/08/01
Tag:dementia assessment; neuropsychological tests; pathological speech; speech biomarkers
Pagenumber:1980-1984
Konferenzangabe:Interspeech 2024, 1-5 September 2024, Kos, Greece
institutes:Fakultät Informatik
Zentrum für Künstliche Intelligenz (KIZ)
Research Themes:Digitalisierung & Künstliche Intelligenz
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