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We present a multilinear statistical model of the human tongue that capturesanatomical and tongue pose related shape variations separately. The model isderived from 3D magnetic resonance imaging data of 11 speakers sustainingspeech related vocal tract configurations. The extraction is performed by usinga minimally supervised method that uses as basis an image segmentation approachand a template fitting technique. Furthermore, it uses image denoising to dealwith possibly corrupt data, palate surface information reconstruction to handlepalatal tongue contacts, and a bootstrap strategy to refine the obtainedshapes. Our evaluation concludes that limiting the degrees of freedom for theanatomical and speech related variations to 5 and 4, respectively, produces amodel that can reliably register unknown data while avoiding overfittingeffects. Furthermore, we show that it can be used to generate a plausibletongue animation by tracking sparse motion capture data.

A Multilinear Tongue Model Derived from Speech Related MRI Data of the Human Vocal Tract