Maximum likelihood successive state splitting

Modeling contextual variations of phones is widely accepted as an important aspect of a continuous speech recognition system, and much research has been devoted to finding robust models of context for HMM systems. In particular, decision tree clustering has been used to tie output distributions across pre-defined states, and successive state splitting (SSS) has been used to define parsimonious HMM topologies. We describe a new HMM design algorithm, called maximum likelihood successive state splitting (ML-SSS), that combines advantages of both these approaches. Specifically, an HMM topology is designed using a greedy search for the best temporal and contextual splits using a constrained EM algorithm. In Japanese phone recognition experiments, ML-SSS shows recognition performance gains and training cost reduction over SSS under several training conditions