Research of Speaker Recognition based on Dipartite GMM
Chengguo Lv · Intelligent Computer and Applications · 2011
Characteristic modeling plays an important role in technology of Speaker Recognition.The modeling method will seriously impact on the performance of speaker recognition system.This article is based on the main model of Text-independent Speaker Recognition,analyses gender differences in pronunciation and increases the differences between the speakers as a starting point,then introduces the idea of competition and UBM into Gaussian Mixture Model(GMM) at the time of characteristic modeling.Therefore,a new approach of characteristic modeling is dipartite which is presented.The method overcomes the limitation that there are plentiful training samples for traditional GMM and the shortcoming that the distributions of all speakers are unified for UBM.Finally,it may build dipartite models for every speaker.The experiment shows that the model based on dipartite algorithm has higher performance than traditional GMM algorithm.