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

Abstract : We present a multilinear statistical model of the human tongue that captures anatomical and tongue pose related shape variations separately. The model was derived from 3D magnetic resonance imaging data of 11 speakers sustaining speech related vocal tract configurations. The extraction was performed by using a minimally supervised method that uses as basis an image segmentation approach and a template fitting technique. Furthermore, it uses image denoising to deal with possibly corrupt data, palate surface information reconstruction to handle palatal tongue contacts, and a bootstrap strategy to refine the obtained shapes. Our experiments concluded that limiting the degrees of freedom for the anatomical and speech related variations to 5 and 4 respectively produces a model that can reliably register unknown data while avoiding overfitting effects.
Type de document :
Pré-publication, Document de travail
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Contributeur : Alexander Hewer <>
Soumis le : vendredi 16 décembre 2016 - 17:18:30
Dernière modification le : mercredi 23 janvier 2019 - 11:26:02
Document(s) archivé(s) le : mardi 21 mars 2017 - 11:38:22


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  • HAL Id : hal-01418460, version 1
  • ARXIV : 1612.05005



Alexander Hewer, Stefanie Wuhrer, Ingmar Steiner, Korin Richmond. A Multilinear Tongue Model Derived from Speech Related MRI Data of the Human Vocal Tract. 2016. 〈hal-01418460v1〉



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