arXiv 2502.159162025-06-12cs.CL

Esethu框架：重新构想低资源语言的可持续数据集治理与策划

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

Lelapa AI；Way With Words；RAIL Lab – University of the Witwatersrand（RAIL实验室 – 网络大学威特沃特斯兰德大学）；DSFSI – University of Pretoria（DSFSI – 帕特里克大学）；MBZUAI；Aarya Legal；Masakhane；Independent（独立研究者）

Jenalea Rajab, Anuoluwapo Aremu, Everlyn Asiko Chimoto, Dale Dunbar, Graham Morrissey, Fadel Thior, Luandrie Potgieter, Jessico Ojo, Atnafu Lambebo Tonja, Maush… 展开作者

Jenalea Rajab, Anuoluwapo Aremu, Everlyn Asiko Chimoto, Dale Dunbar, Graham Morrissey, Fadel Thior, Luandrie Potgieter, Jessico Ojo, Atnafu Lambebo Tonja, Maushami Chetty, Wilhelmina NdapewaOnyothi Nekoto, Pelonomi Moiloa, Jade Abbott, Vukosi Marivate, Benjamin Rosman

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AI总结：

本研究提出赋能社区、保障收益公平的低资源语言数据治理Esethu框架及配套社区许可，推出基于该框架的isiXhosa开源语音数据集ViXSD，通过ASR实验验证其可用性，为非洲语言语音资源建设提供社区驱动的可持续方案。

摘要：

本文提出了Esethu框架，这是一款专门为赋能当地社区、确保其语言资源带来的收益公平分配而设计的可持续数据策划框架。该框架由Esethu许可协议（一种新型的以社区为中心的数据许可协议）提供支持。作为概念验证，我们推出了Vuk'uzenzele isiXhosa语音数据集（ViXSD），这是一个在Esethu框架与许可协议下开发的开源语料库。该数据集包含以isiXhosa为母语者的朗读语音，并补充了人口统计与语言元数据，展示了社区驱动的许可与策划原则如何在维护数据创作者利益的同时，弥补非洲语言自动语音识别（ASR）领域的资源缺口。我们阐述了指导数据集开发的框架，概述了Esethu许可协议的条款，介绍了ViXSD的构建方法，并展示了验证ViXSD可用于构建与优化isiXhosa语音驱动应用的ASR实验。

英文摘要：

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 resource. 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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。