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

2025-02-21

AI Summary

To address data scarcity and inequitable benefit distribution in automatic speech recognition (ASR) for low-resource languages—particularly in Africa—this work proposes a community-centered, sustainable data governance framework. Methodologically, it introduces the novel Esethu Community License, ensuring linguistic data sovereignty and equitable benefit-sharing for native speakers; constructs ViXSD—the first open-source, native-speaker-led isiXhosa speech corpus annotated with fine-grained sociolinguistic metadata; and integrates participatory data collection, structured metadata modeling, and ASR validation using Kaldi and Whisper. Results demonstrate that ViXSD substantially improves isiXhosa ASR performance and establishes a reproducible, ethics-technical co-design paradigm for low-resource language AI. This framework advances both methodological innovation—through community-embedded data governance—and practical scalability, offering a transferable model for ethical, inclusive, and sustainable AI development in under-resourced linguistic contexts.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Humans and AI: AI for Accessibility

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Security and Privacy: Data transparency and provenance
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

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 Vuk'uzenzele 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.

Problem

• Sustainable dataset governance
• Low-resource language empowerment
• Community-centric data licensing

Innovation

• Community-centric data license
• Linguistic metadata enrichment
• Automatic speech recognition validation