Effects of tree structure and statistical methods on broad phonetic classification

Abstract

The goal of this project was to develop a system for assigning individual frames of a speech signal to one of four broad phonetic categories: vowel-like, strong fricative, weak fricative, and silence. Classification results were compared from a K-means clustering algorithm and a maximum likelihood distance measure. In addition to the comparison of statistical methods, this study compared classification performance using several tree-structured, decision-making techniques. Training and test data consisted of various combinations of 98 utterances produced by five male and five female speakers. Results showed very little difference between the K-means and maximum likelihood methods. However, the nature of the decision tree had a significant effect on the performance of the classifier. [Work supported by Rome Air Development Center and the Air Force Office of Scientific Research as part of the Northeast Artificial Intelligence Consortium (Contract No. F3060285-C-60008) and by Redcom Laboratories, Victor, NY.]