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.

该研究论文侧重于开发和评估使用 XLS-R 300m 模型的自动语音识别 (ASR) 技术。该研究旨在提高 ASR 在将口语转化为书面文本方面的性能，尤其是印度尼西亚语、爪哇语和日惹语。该论文讨论了测试程序、使用的数据集以及训练和评估 ASR 系统中采用的方法。结果显示，XLS-R 300m 模型在 Word Error Rate (WER) 方面取得了有竞争力的度量结果，但在日惹语和日惹语上性能略有一些妥协。5-gram KenLM 语言模型的整合显著降低了 WER 并提高了 ASR 的准确性。该研究通过解决语言多样性问题，提高了 ASR 技术的发展，并为不同语言的性能优化提供了见解。