Tone classification of syllable-segmented Thai speech based on multilayer perceptron

Abstract

Thai is a monosyllabic, tonal language that makes use of tone to convey lexical information about the meaning of a syllable. Thai has five distinctive tones, and each tone is well represented by a single F0 contour pattern. In general, a Thai syllable with a different tone has a different lexical meaning. Thus, to completely recognize a spoken Thai syllable, a speech recognition system has to not only recognize a base syllable but also to correctly identify a tone. Hence, tone classification of Thai speech is an essential part of a Thai speech recognition system. In this study, a tone classification of syllable-segmented Thai speech, which incorporates the effects of tonal coarticulation, stress and intonation, was developed Automatic syllable segmentation, which performs segmentation on the training and test utterances into syllable units, was also developed. The acoustical features, including fundamental frequency (F0), duration, and energy extracted from the processing syllable and neighboring syllables, were used as the main discriminating features. A multilayer perceptron (MLP) trained by a backpropagation method was employed to classify these features. The proposed system was evaluated on 920 test utterances spoken by five male and three female native Thai speakers who also uttered the training speech. The proposed system achieved an average accuracy rate of 91.36%.