Evolutionary optimization of contexts for phonetic correction in speech recognition systems

• URL: http://arxiv.org/abs/2102.11480v1
• Date: Tue, 23 Feb 2021 04:14:51 GMT
• Title: Evolutionary optimization of contexts for phonetic correction in speech recognition systems
• Authors: Rafael Viana-C\'amara, Diego Campos-Sobrino, Mario Campos-Soberanis
• Abstract summary: 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. This article explores the use of an evolutionary process to generate an optimized context for a specific application domain.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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