Improving on the Limitations of the ASR Model in Low-Resourced Environments Using Parameter-Efficient Fine-Tuning

@inproceedings{ghimire-etal-2024-improving,
    title = "Improving on the Limitations of the {ASR} Model in Low-Resourced Environments Using Parameter-Efficient Fine-Tuning",
    author = "Ghimire, Rupak Raj  and
      Poudyal, Prakash  and
      Bal, Bal Krishna",
    editor = "Lalitha Devi, Sobha  and
      Arora, Karunesh",
    booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2024",
    address = "AU-KBC Research Centre, Chennai, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://aclanthology.org/2024.icon-1.47/",
    pages = "408--415",
    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."
}