The Esethu Framework: Reimagining Sustainable Dataset Governance and Curation for Low-Resource Languages

This paper presents the Esethu Framework, a sustainable data curation framework specifically designed to empower local communities and ensure equitable benefit-sharing from their linguistic resources. This framework is supported by the Esethu license, a novel community-centric data license. As a proof of concept, we introduce the Vukúzenzele isiXhosa Speech Dataset (ViXSD), an open-source corpus developed under the Esethu Framework and License. The dataset, containing read speech from native isiXhosa speakers enriched with demographic and linguistic metadata, demonstrates how community-driven licensing and curation principles can bridge resource gaps in automatic speech recognition (ASR) for African languages while safeguarding the interests of data creators. We describe the framework guiding dataset development, outline the Esethu license provisions, present the methodology for ViXSD, and present ASR experiments validating ViXSD's usability in building and refining voice-driven applications for isiXhosa.

@article{rajab2025_2502.15916,
  title={ The Esethu Framework: Reimagining Sustainable Dataset Governance and Curation for Low-Resource Languages },
  author={ Jenalea Rajab and Anuoluwapo Aremu and Everlyn Asiko Chimoto and Dale Dunbar and Graham Morrissey and Fadel Thior and Luandrie Potgieter and Jessico Ojo and Atnafu Lambebo Tonja and Maushami Chetty and Onyothi Nekoto and Pelonomi Moiloa and Jade Abbott and Vukosi Marivate and Benjamin Rosman },
  journal={arXiv preprint arXiv:2502.15916},
  year={ 2025 }
}