arXiv:2512.02201v3 Announce Type: replace Abstract: This paper introduces Swivuriso, a 3000-hour multilingual speech dataset developed as part of the African Next Voices project, to support the development and benchmarking of automatic speech recognition (ASR) technologies in seven South African languages. Covering agriculture, healthcare, and general domain topics, Swivuriso addresses significant gaps in existing ASR datasets. We describe the design principles, ethical considerations, and data collection procedures that guided the dataset creation. We present baseline results of training/fine
The increasing availability of large language models and the global push for AI development are driving the creation of diverse, localized datasets to address linguistic and regional specificities.
This dataset significantly enhances the capability for robust multilingual AI development in underserved languages, directly impacting the accessibility and utility of advanced AI applications across Africa.
The existence of a large-scale, ethically sourced multilingual speech dataset for South African languages reduces reliance on models trained predominantly on Western languages, fostering more inclusive AI.
- · South African AI researchers
- · African AI developers
- · Local language speakers
- · Global AI ethics proponents
- · Companies relying solely on English-centric ASR
- · Early-stage Western ASR providers in Africa
Improved automatic speech recognition and natural language processing for South African languages.
Accelerated development of localized AI applications and services tailored to the needs of South African populations.
Enhanced digital inclusion and economic opportunities within South Africa and potentially across the broader African continent, driven by more effective AI integration.
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