Duration Distribution Based HMM Speech Recognition Models
Xi Xiao · Dianzi xuebao · 2004
In order to overcome the defects of the duration modeling of homogeneous HMM in 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,which has been demonstrated that it can be identically defined by either the state duration or the state transition probabilities.The speaker independent continuous speech recognition experiments have shown that,by only modeling the state duration in DDBHMM,a significant improvement (17.8% error rate reduction ) has been achieved comparing with the classical HMM .The ideal properties of DDBHMM will give promise to many aspects of speech modeling,such as the modeling of the state duration,speed variation,speech discontinuity and the inter frame correlation.