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This research paper focuses on the development and evaluation of AutomaticSpeech Recognition (ASR) technology using the XLS-R 300m model. The study aimsto improve ASR performance in converting spoken language into written text,specifically for Indonesian, Javanese, and Sundanese languages. The paperdiscusses the testing procedures, datasets used, and methodology employed intraining and evaluating the ASR systems. The results show that the XLS-R 300mmodel achieves competitive Word Error Rate (WER) measurements, with a slightcompromise in performance for Javanese and Sundanese languages. The integrationof a 5-gram KenLM language model significantly reduces WER and enhances ASRaccuracy. The research contributes to the advancement of ASR technology byaddressing linguistic diversity and improving performance across variouslanguages. The findings provide insights into optimizing ASR accuracy andapplicability for diverse linguistic contexts.

XLS-R Deep Learning Model for Multilingual ASR on Low- Resource Languages: Indonesian, Javanese, and Sundanese