We describe a minimally-supervised method for computing a statistical shape space model of the palate surface. The model is created from a corpus of volumetric magnetic resonance imaging (MRI) scans collected from 12 speakers. We extract a 3D mesh of the palate from each speaker, then train the model using principal component analysis (PCA). The palate model is then tested using 3D 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 coordinates are available. In both cases, our approach yields promising results. It can be applied to extract the palate shape from MRI data, and could be useful to other analysis modalities, such as electromagnetic articulography (EMA) and ultrasound tongue imaging (UTI).
@cite_11 used realtime mri to investigate the morphological variation of the palate and the posterior pharyngeal wall. They extracted the shape information from mid-sagittal slices of the vocal tract. Afterwards, they applied a pca to the obtained data to extract the principal modes of variation of both structures. In their study, they found that the obtained principal modes could actually be related to anatomical variation, such as the degree of concavity of the palate. However, this study was restricted to the 2D case.
@cite_11
2132244055
Purpose Adult human vocal tracts display considerable morphological variation across individuals, but the nature and extent of this variation has not been extensively studied for many vocal tract s...