As interest in large language models (LLMs) grows, the importance of accuracy in automatic speech recognition (ASR) has become more pronounced. This is particularly true for lectures that include specialized terminology, where the success rate of traditional ASR models tends to be low, posing a challenging problem. A method to improve ASR performance for specialized terminology using the word frequency difference approach has been proposed. Through experiments and data analysis, we investigate whether this proposal effectively addresses the issue. Additionally, we introduce the power law as the theoretical foundation for the relative frequency

翻译：随着对大型语言模型（LLMs）兴趣的增长，自动语音识别（ASR）准确性的重要性日益凸显。这一问题在包含专业术语的讲座中尤为突出——传统ASR模型的识别成功率往往较低，构成了一个具有挑战性的难题。一种利用词频差异方法提升专业术语ASR性能的方案已被提出。通过实验与数据分析，我们探究该方案是否能有效解决上述问题。此外，我们引入幂律分布作为相对频率的理论基础。