Proceedings of the Annual Meeting of the Cognitive Science Society

English word pronunciation is a challenging knowledge acquisition problem in which general rules are subject to frequent exceptions of an arbitrary nature. W e have developed a supervised learning system, PRO, which learns about English pronunciation by training with words and their dictionary pronunciations. PRO organizes its knowledge in a case-based memory which preserves fragments of training items but does not remember specific training items in their entirety. After P R O has created a Caise Base in response to a training set, it can pronounce novel test words with substantial degrees of success. Test items are processed by generating a search space in the form of a lateral inhibition network and embedding this search speu:ein a larger network that reflects PRO's previous training experience with relevant fragments.Spreading activation and network relaxation are then used to arrive at a preferred pronunciation for the given test item. In this paper we report preliminary test results based on a trainingcorpus of 750 words and a test set of 300 words.