HUANG Hao　  ZHU Jie 

(Department of Electronic Engineering, Shanghai Jiaotong University  Shanghai 200240) 

Received Oct.15, 2007 

Revised Nov.8, 2007  

Abstract  A discriminative framework of tome model integration in continuous speech recognition was proposed. The method uses model dependent weights to scale probabilities of the hidden Markov models based on spectral features and tone models based on tonal features. The weights are discriminatively trained by minimum phone error criterion. Update equation of the model weights based on extended Baum-Welch algorithm is derived. Various schemes of model weight combination are evaluated and a smoothing technique is introduced to make training robust to over fitting. The proposed method is evaluated on tonal syllable output and character output speech recognition tasks. The experimental results show the proposed method has obtained 9.5% and 4.7% relative error reduction than global weight on the two tasks due to a better interpolation of the given models. This proves the effectiveness of discriminative trained model weights for tone model integration.

PACS numbers: 43.70