Evolutionary optimization of contexts for phonetic correction in speech recognition systems

Abstract: 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 LLM 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.