High-speed dynamic magnetic resonance imaging is a highly efficient tool in capturing vocal tract deformation during speech. However, automated quantification of variations in motion patterns during production of different utterances has been a challenging task due to spatial and temporal misalignments between different image datasets. We present a principal component analysis-based deformation characterization technique built on top of established dynamic speech imaging atlases. Two layers of principal components are extracted to represent common motion and utterance-specific motion, respectively. Comparison between two speech tasks with and without nasalization reveals subtle differences on velopharyngeal deformation reflected in the utterance-specific principal components.
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