Data Set Used
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 vol-umetric 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… (More)
Vocal tract magnetic resonance imaging (MRI) has become one of the preferred imaging modalities for the analysis of human speech production. However, the raw image data must be segmented before further analysis can take place. This paper describes a hybrid approach to extract a 3D tongue model from 3D or 2D MRI scans of the vocal tract during speech, which… (More)
1 Motivation In specific application areas, obtaining higher order motion information is of great interest. An example of such information is the Lagrangian strain tensor  that plays a vital role in mechanical engineering. Since this ten-sor is computed by means of first-order motion derivatives, it is tempting to estimate the optical flow field with a… (More)
(2001). Mapping of gravel biotopes and an examination of the factors controlling the distribution, type and diversity of their biological communities.
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… (More)
We present a novel open-source framework for visualizing electromagnetic articulography (EMA) data in real-time, with a modular framework and anatomically accurate tongue and palate models derived by multilinear subspace learning.
We present an end-to-end text-to-speech (TTS) synthesis system that generates audio and synchronized tongue motion directly from text. This is achieved by adapting a 3D model of the tongue surface to an articula-tory dataset and training a statistical parametric speech synthesis system directly on the tongue model parameter weights. We evaluate the model at… (More)
Statement Hereby I confirm that this thesis is my own work and that I have documented all sources used. Herewith I agree that my thesis will be made available through the library of the Com-2 Acknowledgements I want to thank the following people who made this work possible: Prof. Joachim Weickert for accepting my proposal as the topic of my Master's Thesis.… (More)