Tone Recognition of Chinese Continuous Speech

In this paper our approach to the lexical tone recognition of Chinese continuous speech is presented. The Mixed Gaussian Continuous Probability Model (MGCPM) [1] is used for the tone modeling, and the quadric curve is adopted to simulate the Fundamental frequency (F0) contour, whose three coefficients are calculated and taken as the features of the tone models. The tone variety in continuous Chinese speech recognition is an issue that must be faced in the tone modeling. There are two kinds of tone varieties, the change from canonical one to noncanonical one without changing the pitch trend and that from one to another different one. In order to reduce the negative influence caused by the tone varieties, an iterative method is proposed to distinguish the syllables which have tone varieties and remove them from the whole training data, and then the Tone Variety Matrix (TVM) is introduced for improving the performance of tone models. Experiments have been done based on the continuous Chinese speech database named "863" database. The top1 and top2 accuracy for baseline MGCPM is 67% and 90%, while that for MGCPM incorporated with TVM is 70% and 92%.