  Automatic Speech Recognition (ASR) is an area of growing academic and
commercial interest due to the high demand for applications that use it to
provide a natural communication method. It is common for general purpose ASR
systems to fail in applications that use a domain-specific language. Various
strategies have been used to reduce the error, such as providing a context that
modifies the language model and post-processing correction methods. This
article explores the use of an evolutionary process to generate an optimized
context for a specific application domain, as well as different correction
techniques based on phonetic distance metrics. The results show the viability
of a genetic algorithm as a tool for context optimization, which, added to a
post-processing correction based on phonetic representations, can reduce the
errors on the recognized speech.
