Learning String Edit Distance

In many applications, it is necessary to determine the similarity of two strings. A widely-used notion of string similarity is the edit distance: the minimum number of insertions, deletions, and substitutions required to transform one string into the other. In this report, we provide a stochastic model for string edit distance. Our stochastic model allows us to learn the optimal string edit distance function from a corpus of examples. We illustrate the utility of our approach by applying it to the di cult problem of learning the pronunciation of words in conversational speech. In this application, we learn a string edit distance function with one third the error rate of the untrained Levenshtein distance.