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

Abstract : 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).
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https://hal.inria.fr/hal-01192790
Contributeur : Ingmar Steiner <>
Soumis le : vendredi 4 septembre 2015 - 09:20:53
Dernière modification le : mercredi 14 décembre 2016 - 01:07:24
Document(s) archivé(s) le : samedi 5 décembre 2015 - 11:32:00

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Distributed under a Creative Commons Paternité - Pas d'utilisation commerciale - Pas de modification 4.0 International License

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  • HAL Id : hal-01192790, version 1
  • ARXIV : 1602.07679

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Alexander Hewer, Ingmar Steiner, Timo Bolkart, Stefanie Wuhrer, Korin Richmond. A statistical shape space model of the palate surface trained on 3D MRI scans of the vocal tract. 18th International Congress of Phonetic Sciences, Aug 2015, Glasgow, United Kingdom. 2015, <http://www.icphs2015.info/>. <hal-01192790>

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