Topic focusing mechanism for speech recognition based on probabilistic grammar and topic Markov model

This paper describes a new stochastic topic focusing mechanism for reducing the perplexity of natural spoken languages. In this mechanism, a predictive context-free grammar (CFG) parser analyzes input speech and generates grammar-rule sequences. These rule sequences drive a hidden Markov model (HMM), and the current topic is estimated as the HMM state distribution. The CFG rule probabilities are dynamically changed according to this topic state distribution. Evaluation of this mechanism using a large dialog text database confirms that it can effectively reduce the task perplexity