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Automatic Speech Recognition for the Basa\`{a} Language: A Low-Resource Approach
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Sophie Gertrude Ngo Mock, Charles Moudina Varmantchaonala, Paul Dayang, Jean Michel Nlong II, Christopher Gies

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ResearcharXiv cs.CL

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.

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This story was published by arXiv cs.CL and written by Sophie Gertrude Ngo Mock, Charles Moudina Varmantchaonala, Paul Dayang, Jean Michel Nlong II, Christopher Gies. SyncAI.news shows a preview; the complete article is on the publisher's site.

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