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.

中文翻译：

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语音识别系统中用于语音校正的上下文进化优化

由于对使用自动语音识别来提供自然通信方法的应用程序的大量需求，自动语音识别（ASR）是一个在学术和商业上都日益增长的领域。通用ASR系统在使用域特定语言的应用程序中失败是很常见的。已经使用了各种策略来减少错误，例如提供修改语言模型的上下文和后处理校正方法。本文探讨了使用演化过程为特定应用程序域生成优化上下文的方法，以及基于语音距离度量的不同校正技术。结果显示了遗传算法作为上下文优化工具的可行性，并将其添加到基于语音表示的后处理校正中，