---
title: "SpeechHGT: A Multimodal Hypergraph Transformer for Speech-Based Early Alzheimer’s Disease Detection"
authors:
  - "Shagufta Abid"
  - "Dongyu Zhang"
  - "Ahsan Shehzad"
  - "Jing Ren"
  - "Shuo Yu"
  - "Hongfei Lin"
  - "Feng Xia"
doi: "10.24963/ijcai.2025/1015"
url: "https://ijcai.org/proceedings/2025/1015"
pdf: "https://ijcai.org/proceedings/2025/1015.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "9131-9139"
track: "AI4Tech: AI Enabling Technologies"
keywords:
  - "Domain-specific AI4Tech: AI4Care and AI4Health"
  - "Advanced AI4Tech: Multimodal AI4Tech"
  - "Advanced AI4Tech: Data-driven AI4Tech"
---

# 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

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 9131-9139. DOI: [10.24963/ijcai.2025/1015](https://doi.org/10.24963/ijcai.2025/1015)

## 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.