Identification of contextual factors for pronunciation networks

A data-intensive, semiautomatic method is presented for identifying subsets of contextual factors which are useful for predicting the allophonic realizations of dictionary phonemes. The method organizes contextual descriptions of phonological variation into context trees. Context trees are computed using a combination of decision tree induction for factor selection and hierarchical clustering for forming natural groups of factor values. How the resulting context trees can be used to provide allophones in creating pronunciation networks is described. A phoneme-level representation with a flexible context description is used. It allows modeling of effects extending across syllables and word boundaries