XLS-R Deep Learning Model for Multilingual ASR on Low- Resource
  Languages: Indonesian, Javanese, and Sundanese
Panji Arisaputra Alif Tri Handoyo Amalia Zahra
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.