Midsagittal vocal tract contours are analyzed using the functional data analysis (FDA) technique with which a vocal tract contour (VT) can be parameterized by a set of coefficients. Such a parametric representation of the dynamic vocal tract profiles provides a means for normalizing VT contours across speakers and offers interpretability of coefficient variability as the degree of contribution from specific vocal tract regions. It also enables us to examine the differences in VT behaviors as well as inter- and intra-speaker differences across different speech production styles including emotion expression. A set of FDA coefficients can be used as a feature vector of a given VT contour for further modeling. The efficacy of such feature vectors is tested using the Fisher linear discriminant analysis. A cross-validation accuracy of 65.0% was obtained in the task of discriminating four different emotions with combined data points from two speakers. Index Terms: magnetic resonance imaging, vocal tract, emotion, functional data analysis, emotional speech production
['Sungbok Lee', 'Shrikanth Narayanan']
Vocal tract contour analysis of emotional speech by the functional data curve representation