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We describe a minimally-supervised method for computing a statistical shapespace model of the palate surface. The model is created from a corpus ofvolumetric magnetic resonance imaging (MRI) scans collected from 12 speakers.We extract a 3D mesh of the palate from each speaker, then train the modelusing principal component analysis (PCA). The palate model is then tested using3D MRI from another corpus and evaluated using a high-resolution optical scan.We find that the error is low even when only a handful of measured coordinatesare available. In both cases, our approach yields promising results. It can beapplied to extract the palate shape from MRI data, and could be useful to otheranalysis modalities, such as electromagnetic articulography (EMA) andultrasound tongue imaging (UTI).

A statistical shape space model of the palate surface trained on 3D MRI scans of the vocal tract