English Letter-Phoneme Conversion by Stochastic Transducers

Abstract

This chapter describes the use of stochastic transducers to model and to perform the conversion of English word spellings to phonemic equivalents. Generic word structures can be described by a simple regular grammar which usually overgenerates, producing many candidate translations. The ‘best’ candidate is selected based on the maximum likelihood criterion and the stochastic translation is assumed to be a Markov chain. The initial grammar allows any input string to translate to any output string. A set of example translations is used to refine this grammar to a more specific form: the Kleene closure of letter-phoneme correspondences. These correspondences were inferred by segmenting the maximum likelihood alignment of example translations when a special type of transducer movement is found, or when a segmentation point is found in one of the two (orthographic or phonemic) domains. For efficient translation, the transducers were implemented as stochastic generalised sequential Moore machines so that useless intermediate states in translation and Markov probabilities can be eliminated and reduced, respectively. The current translation accuracy on a per symbol basis is 93.7% on a representative 1667 word test set selected from the Oxford Advanced Learners Dictionary.