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Speech recognition systems for the Spanish language, such as Google's,produce errors quite frequently when used in applications of a specific domain.These errors mostly occur when recognizing words new to the recognizer'slanguage model or ad hoc to the domain. This article presents an algorithm thatuses Levenshtein distance on phonemes to reduce the speech recognizer's errors.The preliminary results show that it is possible to correct the recognizer'serrors significantly by using this metric and using a dictionary of specificphrases from the domain of the application. Despite being designed forparticular domains, the algorithm proposed here is of general application. Thephrases that must be recognized can be explicitly defined for each application,without the algorithm having to be modified. It is enough to indicate to thealgorithm the set of sentences on which it must work. The algorithm'scomplexity is $O(tn)$, where $t$ is the number of words in the transcript to becorrected, and $n$ is the number of phrases specific to the domain.

Fixing Errors of the Google Voice Recognizer through Phonetic Distance Metrics