Decision tree based tone modeling for Chinese speech recognition

Because of the tonal nature of Chinese languages, correct recognition of lexical tones is necessary for Chinese speech recognition. In order to incorporate tone information into Chinese speech recognition, three issues need to be addressed: (i) the representation of the syllable pitch contour as well as the tone contour; (ii) the lexical tone probability estimation; and (iii) the integration of tone probabilities into the Viterbi recognition process. In this paper we propose a robust polynomial segmental representation of the pitch contour coupled with a decision tree based tone classifier. We also propose a novel approach of integrating the decision tree tone classifier directly into a single pass recognition process. The proposed approaches were evaluated on tasks of tone classification and tonal-syllable recognition. In regard to tone classification, the robust decision tree gave a tone classification-accuracy of 89% for isolated syllables and 71.2% for the continuous speech. Moreover, by incorporating the decision tree tone classifier into the Viterbi search, the tonal-syllable recognition error rate in continuous speech was reduced by 13.54%.