Pair Language Models for Deriving Alternative Pronunciations and Spellings from Pronunciation Dictionaries

Abstract

Pronunciation dictionaries provide a readily available parallel corpus for learning to transduce between character strings and phoneme strings or vice versa. Translation models can be used to derive character-level paraphrases on either side of this transduction, allowing for the automatic derivation of alternative pronunciations or spellings. We examine finitestate and SMT-based methods for these related tasks, and demonstrate that the tasks have different characteristics ‐ finding alternative spellings is harder than alternative pronunciations and benefits from round-trip algorithms when the other does not. We also show that we can increase accuracy by modeling syllable stress.