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.

We describe the development of a tool to identify the names of persons and their corresponding institutions as they appear in the "carbon copy" or "cc" lists of correspondence, memorandums, emails, and other types of documents.