Active Learning Approach for Fine-Tuning Pre-Trained ASR Model for a low-resourced Language

Description

<p>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 pretrained 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.</p>