{"type":"ScholarlyArticle","title":"SpeechHGT: A Multimodal Hypergraph Transformer for Speech-Based Early Alzheimer’s Disease Detection","authors":[{"name":"Shagufta Abid","slug":"shagufta-abid","given":"Shagufta","family":"Abid","affiliation":""},{"name":"Dongyu Zhang","slug":"dongyu-zhang","given":"Dongyu","family":"Zhang","affiliation":""},{"name":"Ahsan Shehzad","slug":"ahsan-shehzad","given":"Ahsan","family":"Shehzad","affiliation":""},{"name":"Jing Ren","slug":"jing-ren","given":"Jing","family":"Ren","affiliation":""},{"name":"Shuo Yu","slug":"shuo-yu","given":"Shuo","family":"Yu","affiliation":""},{"name":"Hongfei Lin","slug":"hongfei-lin","given":"Hongfei","family":"Lin","affiliation":""},{"name":"Feng Xia","slug":"feng-xia","given":"Feng","family":"Xia","affiliation":""}],"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.","doi":"10.24963/ijcai.2025/1015","url":"https://ijcai.org/proceedings/2025/1015","pdf":"https://ijcai.org/proceedings/2025/1015.pdf","markdown":"https://ijcai.org/proceedings/2025/1015.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1015","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"AI4Tech: AI Enabling Technologies","track":"AI4Tech: AI Enabling Technologies","number":1015,"pages":{"first":9131,"last":9139},"keywords":["Domain-specific AI4Tech: AI4Care and AI4Health","Advanced AI4Tech: Multimodal AI4Tech","Advanced AI4Tech: Data-driven AI4Tech"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}