Hybrid grammar-bigram speech recognition system with first-order dependence model

An experimental PC-based isolated-word sentence recognizer with two competing language models is described. A probabilistic grammar acts as the main language model and gives the best performance for sentences within its scope, and a bigram model services as backup for the exceptions. Automatic language model selection is based on probability. Context-free parse tree probabilities are products of probabilities of the rules invoked. This context-freeness is unrealistic, and a method for imposing limited context dependence on the rules is described, using first-order conditional probabilities controlled by mutual information. The method has the advantage of being data-driven, based on measured joint distributions of pairs of symbols