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copied!<p><strong>Idea:</strong> The way biotechnologists align two protein sequences is as follows: Each sequence is represented as a string on an alphabet as(A/C/G/T - these are different types of proteins, irrelevant for us), where each letter (here, an entry) represents a particular amino acid. The quality of an alignment (its score) is calculated from the similarity of each pair of corresponding entries, and the number and length of the blank entries that need to be inserted to produce that alignment.</p> <p>Same algorithm (<a href="http://en.wikipedia.org/wiki/Needleman%E2%80%93Wunsch_algorithm" rel="noreferrer">http://en.wikipedia.org/wiki/Needleman-Wunsch_algorithm</a>) can be used for pronunciation, from substitution frequencies in a set of alternate pronunciations. Then you can calculate alignment scores to measure the similarity between the two pronunciations in a way that is sensitive to the differences between phonemes. Measures of similarity that can be used here are Levenshtein distance, phoneme error rate, and word error rate.</p> <p><strong>Algorithms</strong> The minimum number of insertions, deletions and substitutions required for transformation of one sequence into another is the Levenshtein distance. More info at <a href="http://php.net/manual/en/function.levenshtein.php" rel="noreferrer">http://php.net/manual/en/function.levenshtein.php</a> Phoneme error rate (PER) is the Levenshtein distance between a predicted pronunciation and the reference pronunciation, divided by the number of phonemes in the reference pronunciation. Word error rate (WER) is the proportion of predicted pronunciations with at least one phoneme error to the total number of pronunciations.</p> <p><strong>Source</strong>: Did an Internship on this at UW-Madison</p>

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