This paper investigates different statistical modeling frameworks for articulatory speech data obtained using real-time (RT) magnetic resonance imaging (MRI). To quantitatively capture the spatio-temporal shaping process of the human vocal tract during speech production a multi-dimensional stream of direct image features is extracted automatically from the MRI recordings. The features are closely related, though not identical, to the tract variables commonly defined in the articulatory phonology theory. The modeling of the shaping process aims at decomposing the articulatory data streams into primitives by segmentation. A variety of approaches are investigated for carrying out the segmentation task including vector quantizers, Gaussian Mixture Models, Hidden Markov Models, and a coupled Hidden Markov Model. We evaluate the performance of the different segmentation schemes qualitatively with the help of a well understood data set which was used in an earlier study of inter-articulatory timing phenomena of American English nasal sounds. © 2010 ISCA.
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