arXiv:2606.31186v1 Announce Type: new Abstract: Spontaneous speech is a vital non-invasive biomarker for Alzheimer's Disease (AD), yet many systems overlook non-linear structural disruptions and clinical heterogeneity in pathological language. We propose a Multi-View Gated Graph Attention Network that transcribes audio via Automatic Speech Recognition (ASR) to construct semantic, dependency, and co-occurrence graphs, characterizing speech through a "content-structure-flow" framework. Notably, the co-occurrence graph leverages Pointwise Mutual Information (PMI) from a normative corpus to quanti
The paper leverages recent advancements in Graph Neural Networks and Automatic Speech Recognition to address complex linguistic biomarkers for Alzheimer's, indicating a maturation of AI methodologies for medical diagnostics.
This development represents a significant step towards scalable, non-invasive, and early detection of Alzheimer's Disease, potentially transforming clinical diagnosis and intervention strategies.
The ability to characterize pathological language via multi-graph fusion offers a more nuanced and accurate diagnostic tool compared to existing methods, moving beyond traditional linear analysis.
- · AI healthcare diagnostic firms
- · Patients with Alzheimer's and their families
- · Geriatric medicine specialists
- · Speech recognition technology developers
- · Traditional cognitive assessment providers
- · Late-stage Alzheimer's drug developers (if early detection enables prevention)
Improved early diagnosis of Alzheimer's Disease becomes possible through automated speech analysis.
The widespread adoption of such AI tools could lead to earlier clinical trials and interventions, potentially slowing disease progression.
Advances in non-invasive early disease detection globally could shift healthcare budgets towards preventive care and personalized medicine.
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Read at arXiv cs.CL