Duration-Distribution-Based HMM for Speech Recognition  

作　　者：WANG Zuo-ying XIAO Xi 

机构地区：[1]Department of Electronic Engineering,Tsinghua University,Beijing 100084,China

出　　处：《Frontiers of Electrical and Electronic Engineering in China》2006年第1期26-30,共5页中国电气与电子工程前沿（英文版）

摘　　要：To overcome the defects of the duration modeling in the homogeneous Hidden Markov Model(HMM)for speech recognition,a duration-distribution-based HMM(DDBHMM)is proposed in this paper based on a formalized definition of a left-to-right inhomogeneous Markov model.It has been demonstrated that it can be identically defined by either the state duration or the state transition probability.The speaker-independent continuous speech recognition experiments show that by only modeling the state duration in DDBHMM,a significant improvement(17.8%error rate reduction)can be achieved compared with the classical HMM.The ideal properties of DDBHMM give promise to many aspects of speech modeling,such as the modeling of the state duration,speed variation,speech discontinuity,and interframe correlation.

关 键 词：DURATION speech recognition DDBHMM 

分 类 号：TN9[电子电信—信息与通信工程]