Parameterization and automatic labeling of Hungarian intonation

In Hungarian intonation research the goal of a common framework developed by Varga (2002; [1]) is to categorize the intonation within the domain of accent groups by character contours. We propose a linear parameterization of a subset of these contours derived from polynomial stylization. These parameters were used to train classification trees and support vector machines for contour prediction. Parameter extraction and training was carried out on the original F0 contours of spontaneous speech data as well as on three differently normalized variants suppressing fundamental frequency level and range effects. The highest accuracies were obtained for classification trees and F0 residuals after midline subtraction, but the overall performances were rather poor. Nevertheless, a significant improvement of the results was achieved by a Hidden Markov model to predict the correct label sequence from the partly erroneous classification output.