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ZENODO
Article . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
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Empowering African Languages Through Machine Translation and Artificial Intelligence

Authors: Ozioma Anyawuike; Iroetugo Edith Ruth;

Empowering African Languages Through Machine Translation and Artificial Intelligence

Abstract

Thanks to their artificial potential - deep learning AI and machine translation - it appears that African indigenous languages, several of which fit the description of low-resource and endangered, are set to profit. Artificial Intelligence (AI) technologies, including voice recognition software, natural language processing (NLP), and neural machine translation (NMT), give inventive strategies for documenting, recording and reviving endangered languages in a continent that boasts of over 2,000 spoken languages. Projects such as Mozilla’s Common Voice and the Masakhane project demonstrate the role that collaborative efforts in communities can play in developing AI models that are linguistically appropriate for African languages. This paper explores how AI powered solutions can facilitate digital inclusion, encourage multilingual education, and eradicate language barriers by allowing people to use technology in their own preferential language. But there are still a lot of issues, like standardized orthographies, a lack of digital sources, and regional differences in AI development. In addressing these challenges, it is paramount to adopt ethical AI frameworks that appreciate Africa’s diversity, local participation and sustained funding.

Related Organizations
Keywords

Artificial intelligence (AI), Indigenous languages, Neural machine translation (NMT), Natural language processing (NLP), Digital inclusion

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average