An explainable multimodal speech framework identified two speech sub-phenotypes in an Alzheimer's cohort, one mathematically close to a depression source centroid in a shared latent space, with 92.73% proxy cluster separability
Addressing the difficulty of characterising depression in Alzheimer's disease (AD), this study built an explainable multimodal framework integrating linguistic and acoustic features from elicited picture-description speech, used a source-informed cross-dataset transfer strategy to compensate for the absence of directly observed depression labels in the target AD cohort, identified two distinct speech sub-phenotypes via unsupervised clustering, found that one cluster showed mathematical proximity (cosine similarity in a shared latent space) to the depression centroid of a depression-labelled source corpus, then validated cluster separability with a proxy supervised classification task in which the retained multimodal artificial neural network achieved 92.