As the interest in large language models grows, the importance of accuracy in automatic speech recognition has become more pronounced. This is especially true for lectures that include specialized terminology. In such cases, the success rate of traditional ASR models tends to be low, presenting a significant challenge. A method using the word frequency difference approach has been proposed to improve ASR performance for specialized terminology. We investigated this proposal through experiments and data analysis to determine if it effectively addresses the issue. In addition, we introduced the power law as the theoretical foundation for the relative frequency methodology mentioned in this approach.

翻译：随着大型语言模型关注度的提升，自动语音识别的准确性变得尤为重要。对于包含专业术语的讲座内容而言，这种情况尤为明显。传统ASR模型在此类场景下的识别成功率往往偏低，这构成了重大挑战。已有研究提出采用词频差异法来提升专业术语的ASR性能。我们通过实验与数据分析验证了该方法的有效性，以确认其是否切实解决了该问题。此外，我们引入幂律分布作为该方法中相对频率技术的理论基础。