BAYESIAN BELIEF NETWORKS AS A TOOL FOR STOCHASTIC PARSING

Bayesian Belief Networks are a powerful tool for combining different knowledge sources with various degrees of uncertainty in a mathematical sound and computationally efficient way. Surprisingly they have not yet found their way into the speech processing field, despite the fact that in this science multiple unreliable information sources exist. The present paper shows how the theory can be utilized in for language modeling. After providing an introduction to the theory of Bayesian Networks, we develop several extensions to the classic theory by describing mechanisms for dealing with statistical dependence among daughter nodes (usually assumed to be conditionally independent) and by providing a learning algorithm based on the EM-algorithm with which the probabilities of link matrices can be learned from example data. Using these extensions a language model for speech recognition based on a context-free framework is constructed. In this model, sentences are not parsed in their entirety, as is usual with grammatical description, but only ''locally'' on suitably located segments. The model was evaluated over a text data base. In terms of test set entropy the model performed at least as good as the bi/tri-gram models, while showing a good ability to generalize from training to test data.