  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's
language model or ad hoc to the domain. This article presents an algorithm that
uses Levenshtein distance on phonemes to reduce the speech recognizer's errors.
The preliminary results show that it is possible to correct the recognizer's
errors significantly by using this metric and using a dictionary of specific
phrases from the domain of the application. Despite being designed for
particular domains, the algorithm proposed here is of general application. The
phrases that must be recognized can be explicitly defined for each application,
without the algorithm having to be modified. It is enough to indicate to the
algorithm the set of sentences on which it must work. The algorithm's
complexity is $O(tn)$, where $t$ is the number of words in the transcript to be
corrected, and $n$ is the number of phrases specific to the domain.

Computation and Language;Audio and Speech Processing