Speech-based Alzheimer's disease (AD) assessments increasingly rely on pretrained self-supervised learning (SSL) models that learn acoustic representations directly from raw audio, exposing the model to recording factors. We ask whether such factors are merely encoded in SSL representations or can systematically alter...
Serli Kopar, Alkis Koudounas, R. Rane et al.· 0 citations
A new model family (CortexMAE) trained using the masked autoencoder framework is introduced, and in this setting the CortexMAE family outperforms prior models by a large margin, and the first open evaluation suite (Brainmarks) for fMRI foundation models is released.
Connor Lane, Mihir Tripathy, L. Murali et al.· arXiv.org· 5 citations· ⚡1
The findings suggest that robust, individualized brain-based predictions for mental health outcomes remain challenging with the available data and sample size.
Marija Tochadse, Julia Klawohn, Christian Kaufmann et al.· Scientific Reports· 0 citations
A layer-wise analysis of nine SSL speech backbones using a low-capacity logistic regression probe reveals that the transferred discriminative signal lacks pathological specificity, highlighting critical limitations that must be addressed before speech-based pathology recognition models can be reliably deployed in clini...
Serli Kopar, Sam Gijsen, Abner Hernandez et al.· 0 citations
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