AN INVESTIGATION OF VELOPHARYNGEAL CLOSURE WITH LINEAR REGRESSION

Abstract

Cleft lip and palate is a common birth defect in the United States. Children diagnosed with this abnormality face difficulties during feeding, hearing and speech. Surgical methods exist to repair the cleft lip and palate but often require subsequent surgeries as children are unable to gain full speech capabilities as they tend to develop hypernasal speech due to velopharyngeal inadequacy. Investigating velopharyngeal closure can help speech pathologists, surgeons and related professionals understand the effect of velopharyngeal anatomy on velopharyngeal function. In order to accomplish this, several studies have used two dimensional and three dimensional modeling to visualize the velum. Very few attempts have been made to track the velum and plot its movement against time. Image segmentation has been used widely for various purposes. However, its proficiency in tracking the velum is questionable at the moment. Two image segmentation methods, EdgeTrak and the Hidden Markov Model, are reviewed in this report. EdgeTrak, a software developed at the Video/Image Modeling and Synthesis Laboratory, has been proven to track the surface of a human tongue during speech production. An attempt was made to similarly track the velum during speech production using EdgeTrak but the results were disappointing. Also, synchronized audio mapping using the Hidden Markov Model was only partially successful. This report describes the challenges image segmentation faces with regards to tracking the velum.