Latent Manifold Rectification for low-resource multimodal speech-based Alzheimer’s disease detection

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Speech-based Alzheimer’s disease (AD) detection is commonly formulated as discrete classification, although diagnostic labels may also exhibit an ordered cross-sectional structure. This paper proposes Latent Manifold Rectification (LMR) for low-resource multimodal AD classification from speech. The framework combines a multimodal variational autoencoder with topology-aware latent rectification and trajectory-interpolated consistency regularization (TICR). For NCMMSC2021, HC, MCI, and AD are treated as supervised classes; an MCI-aware soft disease-axis constraint encourages flexible HC–MCI–AD ordering, and TICR operates along the adjacent HC–MCI and MCI–AD transitions. For ADReSS, where MCI labels are unavailable, the MCI-related term is disabled and TICR reduces to HC–AD interpolation. Under speaker-independent 10-fold cross-validation, the A+T TICR model achieves accuracies of 0.8487 ± 0.0233 on NCMMSC2021 and 0.8300 ± 0.0286 on ADReSS. Evaluation on the official ADReSS Challenge split yields 0.8400 ± 0.0126 over five seeds. The main classification experiments show higher mean performance for LMR than for the corresponding baselines, although the corrected paired comparisons do not establish statistically significant superiority. Separately, quantitative analysis of the NCMMSC2021 latent space reveals a strong but non-rigid HC–MCI–AD ordered component, with Spearman 𝜌 = 0 . 8 7 3 together with substantial off-axis variation. These findings distinguish the observed classification trends from the evidence for diagnostic organization in the learned representation and support LMR as a diagnosis-aware regularization framework for low-resource AD classification.