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        <title>Improving on the Limitations of the ASR Model in Low-Resourced Environments Using Parameter-Efficient Fine-Tuning</title>
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        <namePart type="given">Rupak</namePart>
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    <abstract>Modern general-purpose speech recognition systems are more robust in languages with high resources. In contrast, achieving state-of-the-art accuracy for low-resource languages is still challenging. The fine-tuning of the pre-trained model is one of the highly popular practices which utilizes the existing information while efficiently learning from a small amount of data to enhance the precision and robustness of speech recognition tasks. This work attempts to diagnose the performance of a pre-trained model when transcribing the audio from the low-resource language. In this work, we apply an adapter-based iterative parameter-efficient fine-tuning strategy on a limited dataset aiming to improve the quality of the transcription of a previously fine-tuned model. For the experiment we used Whisper‘s multilingual pre-trained speech model and Nepali as a test language. Using this approach we achieved Word Error Rate of 27.9%,which is more than 19% improvement over pre-trained Whisper Large \ensuremath- V2.</abstract>
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%0 Conference Proceedings
%T Improving on the Limitations of the ASR Model in Low-Resourced Environments Using Parameter-Efficient Fine-Tuning
%A Ghimire, Rupak Raj
%A Poudyal, Prakash
%A Bal, Bal Krishna
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F ghimire-etal-2024-improving
%X Modern general-purpose speech recognition systems are more robust in languages with high resources. In contrast, achieving state-of-the-art accuracy for low-resource languages is still challenging. The fine-tuning of the pre-trained model is one of the highly popular practices which utilizes the existing information while efficiently learning from a small amount of data to enhance the precision and robustness of speech recognition tasks. This work attempts to diagnose the performance of a pre-trained model when transcribing the audio from the low-resource language. In this work, we apply an adapter-based iterative parameter-efficient fine-tuning strategy on a limited dataset aiming to improve the quality of the transcription of a previously fine-tuned model. For the experiment we used Whisper‘s multilingual pre-trained speech model and Nepali as a test language. Using this approach we achieved Word Error Rate of 27.9%,which is more than 19% improvement over pre-trained Whisper Large \ensuremath- V2.
%U https://aclanthology.org/2024.icon-1.47/
%P 408-415

Markdown (Informal)

[Improving on the Limitations of the ASR Model in Low-Resourced Environments Using Parameter-Efficient Fine-Tuning](https://aclanthology.org/2024.icon-1.47/) (Ghimire et al., ICON 2024)

• Improving on the Limitations of the ASR Model in Low-Resourced Environments Using Parameter-Efficient Fine-Tuning (Ghimire et al., ICON 2024)

ACL

• Rupak Raj Ghimire, Prakash Poudyal, and Bal Krishna Bal. 2024. Improving on the Limitations of the ASR Model in Low-Resourced Environments Using Parameter-Efficient Fine-Tuning. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 408–415, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).