Incremental adaptation based on a macroscopic time evolution system

In this paper, we propose a new incremental model adaptation approach based on posterior distributions of model parameters. We consider a propagation mechanism of the posterior distributions whereby that the process of posterior refinement is modeled analytically. Then, we derive an incremental estimation algorithm based on a time evolution system, which explicitly includes a discrete stochastic process unlike the conventional Bayesian approaches. This algorithm is viewed as a general solution of the Kalman filter algorithm, where posterior distributions make a transition after every input of an utterance set, and where the evolutions of posterior distributions are represented on a macroscopic time scale.