Automatic learning of structural language models

A novel approach to adaptive language acquisition is proposed. This approach is based on the pattern recognition framework of interpretation, and models the acoustic, lexical, syntactic, and semantic constraints of a given continuous speech task through the concept of sequential finite-state transduction. In order to automatically learn the required finite-state models from training data, a grammatical inference procedure is applied which directly uses a previously introduced error-correcting grammatical inference algorithm. Experiments with relatively simple but nontrivial continuous speech understanding tasks are presented, with results showing both the viability and appropriateness of the proposed approach