Improved discriminative training for generative model  

作　　者：WU Ya-hui,GUO Jun,LIU Gang Laboratory of Pattern Recognition and Intelligent System,Beijing University of Posts and Telecommunications,Beijing 100876,China 

出　　处：《The Journal of China Universities of Posts and Telecommunications》2009年第3期126-130,共5页中国邮电高校学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(60705019);the Hi-Tech Research and Development Program of China(2006AA010102,2007AA01Z417)

摘　　要：This article proposes a model combination method to enhance the discriminability of the generative model. Generative and discriminative models have different optimization objectives and have their own advantages and drawbacks. The method proposed in this article intends to strike a balance between the two models mentioned above. It extracts the discriminative parameter from the generative model and generates a new model based on a multi-model combination. The weight for combining is determined by the ratio of the inter-variance to the intra-variance of the classes. The higher the ratio is, the greater the weight is, and the more discriminative the model will be. Experiments on speech recognition demonstrate that the performance of the new model outperforms the model trained with the traditional generative method.This article proposes a model combination method to enhance the discriminability of the generative model. Generative and discriminative models have different optimization objectives and have their own advantages and drawbacks. The method proposed in this article intends to strike a balance between the two models mentioned above. It extracts the discriminative parameter from the generative model and generates a new model based on a multi-model combination. The weight for combining is determined by the ratio of the inter-variance to the intra-variance of the classes. The higher the ratio is, the greater the weight is, and the more discriminative the model will be. Experiments on speech recognition demonstrate that the performance of the new model outperforms the model trained with the traditional generative method.

关 键 词：model combination DISCRIMINABILITY generative model TRAINING speech recognition 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]