Tag
This paper investigates the interpretability of DAPF-based models for dementia detection, revealing that while DAPF achieves strong performance, its token-level explanations lack faithfulness.
This paper proposes a Multi-View Gated Graph Attention Network for Alzheimer's Disease detection from spontaneous speech, using semantic, dependency, and co-occurrence graphs with an adaptive gated fusion mechanism. The model achieves 90.00% accuracy on the ADReSSo dataset, and the source code is publicly available.
This paper proposes a cross-linguistic transfer learning approach for detecting Alzheimer's Disease from speech across multiple languages, achieving F1 scores of 82% and supporting real-time screening applications.