NarraSpace: Multimodal Scene-Grounded Evidence for Picture-Description Dementia Classification
Picture-description dementia classification uses a shared visual scene, but a transcript-level prediction alone does not identify the scene evidence underlying its inputs.
ProofPaper ↗
Key points
- NarraSpace links words and speech times to named picture elements and retrieves utterances for predefined scene events.
- Narrative-spatial descriptors summarize coverage and temporal organization.
- A frozen language model contrasts each event's text under examples from Alzheimer's disease and healthy-control training participants, alongside transcript surprisal and coverage measurements.
- On the official 48-participant test set of the Alzheimer's Dementia Recognition through Spontaneous Speech benchmark, a fixed support vector classifier achieves $80.88%\pm2.56%$ mean accuracy over fifty seeds; averaged probabilities yield $81.25%$ accuracy and $91.32%$ area under the receiver operating characteristic curve.
Sources (1)
- [1]NarraSpace: Multimodal Scene-Grounded Evidence for Picture-Description Dementia ClassificationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 05:55 PM
Picture-description dementia classification uses a shared visual scene, but a transcript-level prediction alone does not identify the scene evidence underlying its inputs.
NarraSpace links words and speech times to named picture elements and retrieves utterances for predefined scene events.
Extractive summary: sentences quoted from the sources.
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