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

Alif Tri Handoyo; Amalia Zahra; Panji Arisaputra

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

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arxiv 2401.06832 v1 pith:6PQSLVMZ submitted 2024-01-12 cs.CL cs.LGcs.SDeess.AS

Panji Arisaputra , Alif Tri Handoyo , Amalia Zahra This is my paper · ORCID

classification cs.CLcs.LGcs.SDeess.AS

keywords languagesmodeljavaneseperformancesundanesexls-raccuracyindonesian

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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.

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Which Languages Transfer Best to Warlpiri? A Similarity-Based Study for Low-Resource ASR
cs.CL 2026-07 conditional novelty 6.0 of 10

Assamese and Hindi, selected by acoustic and typological similarity to Warlpiri, cut Whisper WER/CER most; acoustic similarity best predicts fine-tuning gains, inventory/typology zero-shot.

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