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 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.
Keywords
Cite
@article{arxiv.2102.11480,
title = {Evolutionary optimization of contexts for phonetic correction in speech recognition systems},
author = {Rafael Viana-Cámara and Diego Campos-Sobrino and Mario Campos-Soberanis},
journal= {arXiv preprint arXiv:2102.11480},
year = {2021}
}
Comments
13 pages, 4 figures, This article is a translation of the paper "Optimizaci\'on evolutiva de contextos para la correcci\'on fon\'etica en sistemas de reconocimiento del habla" presented in COMIA 2019