Topology Estimation of Hierarchical Hidden Markov Models for Language Models

Estimation of topology of probabilistic models provides us with an important technique for many statistical language processing tasks. In this investigation, we propose a new topology estimation method for Hierarchical Hidden Markov Model (HHMM) that generalizes Hidden Markov Model (HMM) in a hierarchical manner. HHMM is a stochastic model which has powerful description capability compared to HMM, but it is hard to estimate HHMM topology because we have to give an initial hierarchy structure in advance on which HHMM depends. In this paper we propose a recursive estimation method of HHMM submodels by using frequent similar subsequence sets. We show some experimental results to see the effectiveness of our method.