@inproceedings{rupak-raj-etal-2023-active,
    title = "Active Learning Approach for Fine-Tuning Pre-Trained {ASR} Model for a Low-Resourced Language: A Case Study of {N}epali",
    author = "Ghimire, Rupak Raj  and
      Bal, Bal Krishna  and
      Poudyal, Prakash",
    editor = "D. Pawar, Jyoti  and
      Lalitha Devi, Sobha",
    booktitle = "Proceedings of the 20th International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2023",
    address = "Goa University, Goa, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.icon-1.9/",
    pages = "82--89",
    abstract = "Fine tuning of the pre-trained language model is a technique which can be used to enhance the technologies of low-resourced languages. The unsupervised approach can fine-tune any pre-trained model with minimum or even no language-specific resources. It is highly advantageous, particularly for languages that possess limited computational resources. We present a novel approach for fine-tuning a pre-trained Automatic Speech Recognition (ASR) model that is suitable for low resource languages. Our methods involves iterative fine-tuning of pre-trained ASR model. mms-1b is selected as the pretrained seed model for fine-tuning. We take the Nepali language as a case study for this research work. Our approach achieved a CER of 6.77{\%}, outperforming all previously recorded CER values for the Nepali ASR Systems."
}