Acquisition et modélisation de données articulatoires dans un contexte multimodal

Michael Aron 1
1 MAGRIT - Visual Augmentation of Complex Environments
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : There is no single technique that will allow all relevant behaviour of the speech articulators (lips, tongue, palate...) to be spatially ant temporally acquired. Thus, this thesis investigates the fusion of multimodal articulatory data. A framework is described in order to acquire and fuse automatically an important database of articulatory data. This includes: 2D Ultrasound (US) data to recover the dynamic of the tongue, stereovision data to recover the 3D dynamic of the lips, electromagnetic sensors that provide 3D position of points on the face and the tongue, and 3D Magnetic Resonance Imaging (MRI) that depict the vocal tract for various sustained articulations. We investigate the problems of the temporal synchronization and the spatial registration between all these modalities, and also the extraction of the shape articulators from the data (tongue tracking in US images). We evaluate the uncertainty of our system by quantifying the spatial and temporal inacuracies of the components of the system, both individually and in combination. Finally, the fused data are evaluated on an existing articulatory model to assess their quality for an application in speech production.
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Michael Aron. Acquisition et modélisation de données articulatoires dans un contexte multimodal. Interface homme-machine [cs.HC]. Université Henri Poincaré - Nancy I, 2009. Français. ⟨tel-00432124v2⟩

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