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        <title>Active Learning Approach for Fine-Tuning Pre-Trained ASR Model for a Low-Resourced Language: A Case Study of Nepali</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Active Learning Approach for Fine-Tuning Pre-Trained ASR Model for a Low-Resourced Language: A Case Study of Nepali
%A Ghimire, Rupak Raj
%A Bal, Bal Krishna
%A Poudyal, Prakash
%Y D. Pawar, Jyoti
%Y Lalitha Devi, Sobha
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F rupak-raj-etal-2023-active
%X 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.
%U https://aclanthology.org/2023.icon-1.9/
%P 82-89

Markdown (Informal)

[Active Learning Approach for Fine-Tuning Pre-Trained ASR Model for a Low-Resourced Language: A Case Study of Nepali](https://aclanthology.org/2023.icon-1.9/) (Ghimire et al., ICON 2023)

• Active Learning Approach for Fine-Tuning Pre-Trained ASR Model for a Low-Resourced Language: A Case Study of Nepali (Ghimire et al., ICON 2023)

ACL

• Rupak Raj Ghimire, Bal Krishna Bal, and Prakash Poudyal. 2023. Active Learning Approach for Fine-Tuning Pre-Trained ASR Model for a Low-Resourced Language: A Case Study of Nepali. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 82–89, Goa University, Goa, India. NLP Association of India (NLPAI).