Vocal tract airway tissue boundary tracking for rtMRI using shape and appearance priors

Abstract

Knowledge about the dynamic shape of the vocal tract is the basis of many speech production applications such as, articulatory analysis, modeling and synthesis. Vocal tract airway tissue boundary segmentation in the mid-sagittal plane is necessary as an initial step for extraction of the cross-sectional area function. This segmentation problem is however challenging due to poor resolution of real-time speech MRI, grainy noise and the rapidly varying vocal tract shape. We present a novel approach to vocal tract airway tissue boundary tracking by training a statistical shape and appearance model for human vocal tract. We manually segment a set of vocal tract profiles and utilize a statistical approach to train a shape and appearance model for the tract. An active contour approach is employed to segment the airway tissue boundaries of the vocal tract while restricting the curve movement to the trained shape and appearance model. Then the contours in subsequent frames are tracked using dense motion estimation methods. Experimental evaluations over the mean square error metric indicate significant improvements compared to the state-of-the-art.