Abstract
Mandarin is a tonal language, in which each syllable is assigned a tone (a total of five tone types). In general, the tonality of a Mandarin syllable is characterized by its corresponding pitch contour. In view of this, we adopt several acoustic features related to pitch information in this study. Besides, since tone is usually influenced by different pronunciations and the sandhi rules, we accordingly add inter-syllabic acoustic features. Once these features are available, we apply two popular classifiers, Gaussian mixture model (GMM) and support vector machine (SVM) to proceed with the tone recognition. In addition, we also try to use the sequential floating search method (SFSM) to perform feature selection. In this study, two datasets, Corpus455 and TangPoem, are used to conduct several experiments. The experimental results indicate that the number of dimensionality is reduced from 30 to 8 for Corpus455 database, whereas it is reduced from 30 to 10 for TangPoem while SFSM is adopted. The tone recognition rates of Corpus455 by using GMM+SFSM and SVM+SFSM are promoted about 6.05% and 1.70% respectively as compared with using GMM and SVM only. Similarly, the tone recognition rates of TangPoem are changed about -0.61% and 10.43%.