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Learning String Edit Distance (revised October 1997)

Report ID:
September 1996
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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 difficult 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.

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