Speaker conversion through non-linear frequency warping of straight spectrum

Abstract

This paper presents our approach to automatically detect tone nuclei, and to use their features for recognizing lexical tones of Chinese continuous speech. We have suggested that Fundamental frequency (F0) contour of a syllable usually consists of three segments: onset course, tone nucleus and offset course. Among them, only tone nucleus contains key features for tone discrimination, hence the tone critical segment of a syllable. The other two segments result from physiological transition effect of human vocal cords, and are affected largely by adjacent tones in continuous speech. The tone nucleus can be detected out by a two-process scheme; the first process segments a syllable F0 contour by Segmental Clustering algorithm, and the second one finds tone nucleus according to knowledge rules on suprasegmental features. Tone recognition performance can be improved by using tone nucleus features and discarding others. Tone recognition experimental results proved the advantage of our method over the conventional ones.