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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 way of communication. It is common for general purpose ASR systems to fail in certain applications that use a domain specific language. Different 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 is able to reduce the errors on the recognized speech.

Optimización evolutiva de contextos para la corrección fonética en sistemas de reconocimiento del habla