MINT: Multimodal Imaging-to-Speech Knowledge Transfer for Early Alzheimer's Screening

Abstract:Alzheimer's disease is a progressive neurodegenerative disorder in which mild cognitive impairment (MCI) precedes dementia. Structural MRI provides biomarkers but requires costly infrastructure, limiting population-scale deployment. Speech offers a non-invasive alternative, yet speech-only classifiers are developed independently of neuroimaging and lack biological grounding for CN-versus-MCI classification. We propose MINT (Multimodal Imaging-to-Speech Knowledge Transfer), a three-stage framework that transfers MRI-derived biomarker structure to speech during training. An MRI teacher defines a compact embedding space for CN-versus-MCI classification, while a residual projection head aligns speech representations to this space using a combined geometric loss. The frozen MRI classifier enables imaging-free inference. On ADNI-4, aligned speech achieves performance comparable to speech baselines, while multimodal fusion improves over MRI alone. Ablations identify dropout regularization and self-supervised pretraining as important design choices. To our knowledge, MINT is the first demonstration of MRI-to-speech knowledge transfer for early Alzheimer's screening without imaging at inference.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2602.23994 [cs.LG]

(or arXiv:2602.23994v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2602.23994 Focus to learn more arXiv-issued DOI via DataCite

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