  Speech recognition, especially name recognition, is widely used in phone
services such as company directory dialers, stock quote providers or location
finders. It is usually challenging due to pronunciation variations. This paper
proposes an efficient and robust data-driven technique which automatically
learns acceptable word pronunciations and updates the pronunciation dictionary
to build a better lexicon without affecting recognition of other words similar
to the target word. It generalizes well on datasets with various sizes, and
reduces the error rate on a database with 13000+ human names by 42%, compared
to a baseline with regular dictionaries already covering canonical
pronunciations of 97%+ words in names, plus a well-trained
spelling-to-pronunciation (STP) engine.
