SpeechHGT: A Multimodal Hypergraph Transformer for Speech-Based Early Alzheimer’s Disease Detection

Abid, Shagufta; Zhang, Dongyu; Shehzad, Ahsan; Ren, Jing; Yu, Shuo; Lin, Hongfei; Xia, Feng

doi:10.24963/ijcai.2025/1015

SpeechHGT: A Multimodal Hypergraph Transformer for Speech-Based Early Alzheimer’s Disease Detection

Shagufta Abid, Dongyu Zhang, Ahsan Shehzad, Jing Ren, Shuo Yu, Hongfei Lin, Feng Xia

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. AI4Tech: AI Enabling Technologies. Pages 9131-9139.

https://doi.org/10.24963/ijcai.2025/1015

Keywords

Domain-specific AI4Tech: AI4Care and AI4Health
Advanced AI4Tech: Multimodal AI4Tech
Advanced AI4Tech: Data-driven AI4Tech

Abstract

Early detection of Alzheimer's disease (AD) through spontaneous speech analysis represents a promising, non-invasive diagnostic approach. Existing methods predominantly rely on fusion-based multimodal deep learning, effectively integrating linguistic and acoustic features. However, these methods inadequately model higher-order interactions between modalities, reducing diagnostic accuracy. To address this, we introduce SpeechHGT, a multimodal hypergraph transformer designed to capture and learn higher-order interactions in spontaneous speech features. SpeechHGT encodes multimodal features as hypergraphs, where nodes represent individual features and hyperedges represent grouped interactions. A novel hypergraph attention mechanism enables robust modeling of both pairwise and higher-order interactions. Experimental evaluations on the DementiaBank datasets reveal that SpeechHGT achieves state-of-the-art performance, surpassing baseline models in accuracy and F1 score. These results highlight the potential of hypergraph-based models to improve AI-driven diagnostic tools for early AD detection.

Shagufta Abid, Dongyu Zhang, Ahsan Shehzad, Jing Ren, Shuo Yu, Hongfei Lin, and Feng Xia. 2025. SpeechHGT: A Multimodal Hypergraph Transformer for Speech-Based Early Alzheimer’s Disease Detection. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 9131–9139. https://doi.org/10.24963/ijcai.2025/1015

@inproceedings{ijcai2025p1015,
  title     = {SpeechHGT: A Multimodal Hypergraph Transformer for Speech-Based Early Alzheimer’s Disease Detection},
  author    = {Abid, Shagufta and Zhang, Dongyu and Shehzad, Ahsan and Ren, Jing and Yu, Shuo and Lin, Hongfei and Xia, Feng},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {9131--9139},
  year      = {2025},
  month     = {8},
  note      = {AI4Tech: AI Enabling Technologies},
  doi       = {10.24963/ijcai.2025/1015},
  url       = {https://doi.org/10.24963/ijcai.2025/1015},
}

TY  - CPAPER
TI  - SpeechHGT: A Multimodal Hypergraph Transformer for Speech-Based Early Alzheimer’s Disease Detection
AU  - Abid, Shagufta
AU  - Zhang, Dongyu
AU  - Shehzad, Ahsan
AU  - Ren, Jing
AU  - Yu, Shuo
AU  - Lin, Hongfei
AU  - Xia, Feng
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 9131
EP  - 9139
DO  - 10.24963/ijcai.2025/1015
UR  - https://doi.org/10.24963/ijcai.2025/1015
ER  -