Synergizing Zero-Shot Cross-Lingual Alzheimer Detection with Language-Invariant Multimodal Bi-Geometric Adversarial Learning

Abstract

In this work, we study zero-shot cross-lingual speech-based Alzheimer's disease detection (SADD). We hypothesize that learning language-invariant multimodal representations by fusing multilingual speech and text pretrained models is essential for reliable transfer to unseen languages, as the two modalities capture complementary acoustic and linguistic markers of cognitive impairment while adversarial learning suppresses language-specific confounds. Empirical results in zero-shot cross-lingual evaluation substantiate the hypothesis, showing that multimodal fusion consistently outperforms unimodal baselines. To this end, we propose ORBIT, a novel framework that combines cross-attentive fusion, multi-tap language adversaries, and complementary spherical--hyperbolic geometric learning with consensus clustering. Across settings, ORBIT achieves the strongest performance compared to unimodal models and simple concatenation-based fusion baselines.