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 a
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 with one fourth the error rate of the untrained
Levenshtein distance. Our approach is applicable to any string classification
problem that may be solved using a similarity function against a database of
labeled prototypes.
Keywords: string edit distance, Levenshtein distance, stochastic
transduction, syntactic pattern recognition, prototype dictionary, spelling
correction, string correction, string similarity, string classification, speech
recognition, pronunciation modeling, Switchboard corpus.

本文提出了一种基于概率的字符串编辑距离模型，并通过应用于口语语音中单词发音的学习来证明其实用性