Tone model integration based on discriminative weight training for Putonghua speech recognition

作     者：HUANG Hao ZHU Jie HUANG Hao ZHU Jie (Department of Electronic Engineering,Shanghai Jiaotong University Shanghai 200240)

作者机构：Department of Electronic Engineering Shanghai Jiaotong University Shanghai 200240 

出 版 物：《Chinese Journal of Acoustics》 (声学学报（英文版）)

年 卷 期：2008年第27卷第3期

页      面：193-202页

学科分类：0501[文学-中国语言文学] 050103[文学-汉语言文字学] 05[文学] 

主　　题：continuous speech recognition model integration hidden Markov models smoothing technique error reduction 

摘      要：A discriminative framework of tone 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 ewluated 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.