This research paper focuses on the development and evaluation of Automatic Speech Recognition (ASR) technology using the XLS-R 300m model. The study aims to improve ASR performance in converting spoken language into written text, specifically for Indonesian, Javanese, and Sundanese languages. The paper discusses the testing procedures, datasets used, and methodology employed in training and evaluating the ASR systems. The results show that the XLS-R 300m model achieves competitive Word Error Rate (WER) measurements, with a slight compromise in performance for Javanese and Sundanese languages. The integration of a 5-gram KenLM language model significantly reduces WER and enhances ASR accuracy. The research contributes to the advancement of ASR technology by addressing linguistic diversity and improving performance across various languages. The findings provide insights into optimizing ASR accuracy and applicability for diverse linguistic contexts.

翻译：本研究论文聚焦于利用XLS-R 300m模型开发和评估自动语音识别（ASR）技术。该研究旨在提升ASR系统将语音转换为书面文本的性能，特别针对印尼语、爪哇语和巽他语。论文讨论了测试流程、所用数据集以及训练和评估ASR系统所采用的方法。结果表明，XLS-R 300m模型在词错误率（WER）指标上表现优异，但在爪哇语和巽他语的性能上略有折中。集成5-gram KenLM语言模型显著降低了WER，并提升了ASR准确性。本研究通过应对语言多样性并提升多语种性能，推动了ASR技术的发展。相关发现为优化不同语言语境下的ASR准确性和适用性提供了见解。