NONSTATIONARY HIDDEN MARKOV MODEL

Abstract

The standard hidden Markov model (HMM) has often been pointed out for its inappropriateness in capturing state duration behavior. Explicit state duration modeling in the HMM has been developed but it is not sufficient for modeling the intrinsically dynamic, or nonstationary, transition process. Nevertheless, most research efforts have been concerned with only within-state nonstationarity, e.g., variable state duration and regional symbol correlation. In this paper we explore the nonstationarity of Markov chains and propose a nonstationary HMM that is defined with a set of dynamic transition probability parameters A(tau) = {a(ij)(tau)}, a function of time duration tau. The model, when compared to the traditional models, is defined as a generalization of the standard HMM and the state duration HMM, with the description being given for discrete observation distributions. Through a set of experiments, it has been shown that the proposed model is more capable of capturing the dynamic nature of signals with higher discrimination power in on-line character recognition.