Transfer Learning for Multilingual Speech Recognition in Low-Resource Languages
Abstract
Speech recognition systems often struggle with low-resource languages due to limited training data. This paper explores the use of transfer learning to improve speech recognition accuracy for such languages. By fine-tuning pre-trained models on multilingual datasets, the proposed approach significantly reduces the data requirements for low-resource languages. Experiments conducted on multiple underrepresented languages demonstrate substantial performance improvements in word error rates compared to baseline models. The study highlights the potential of transfer learning to bridge linguistic gaps, enabling more inclusive and accessible speech recognition technologies.
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