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This paper addresses the critical challenge of applying advanced AI speech recognition techniques to low-resource languages, specifically Basa\`{a}, by leveraging transfer learning and pre-trained models typically reserved for major languages. The research demonstrates how deep neural networks and transformer architectures can be adapted to bridge the performance gap between well-resourced and underrepresented linguistic communities.
Automatic Speech Recognition for the Basa\`{a} Language: A Low-Resource Approach
arXiv:2609.32408v1 Announce Type: new Abstract: The rapid advancement of Artificial Intelligence (AI) and Natural Language Processing (NLP) has revolutionized the way humans interact with machines. Among the most impactful developments is Automatic Speech Recognition (ASR), which enables computers to convert spoken language into text. Systems such as those built on deep neural networks, transformer architectures, and self-supervised learning have achieved near-human performance for well-resourced languages such as English and French. Yet, these advances have disproportionately benefited a small fraction of the world's languages.
Abstract:The rapid advancement of Artificial Intelligence (AI) and Natural Language Processing (NLP) has revolutionized the way humans interact with machines. Among the most impactful developments is Automatic Speech Recognition (ASR), which enables computers to convert spoken language into text. Systems such as those built on deep neural networks, transformer architectures, and self-supervised learning have achieved near-human performance for well-resourced languages such as English and French. Yet, these advances have disproportionately benefited a small fraction of the world's languages.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2609.32408 [cs.CL]
(or arXiv:2609.32408v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.32408 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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