arXiv 2602.027342026-03-02eess.AScs.AIcs.CL

WAXAL：大规模多语言非洲语言语音语料库

WAXAL: A Large-Scale Multilingual African Language Speech Corpus

Google Research（谷歌研究）；University of Ghana（加纳大学）；Digital Umuganda（数字乌姆乌甘达）；Addis Ababa University（亚的斯亚贝巴大学）；Makerere University（Makerere大学）；Loud and Clear Comm. Ltd.（清晰广播有限公司）；Media Trust Ltd.（媒体信托有限公司）；Gates Foundation（比尔及梅琳达·盖茨基金会）；AIMS Senegal（塞内加尔AIMS）

Abdoulaye Diack, Perry Nelson, Kwaku Agbesi, Angela Nakalembe, MohamedElfatih MohamedKhair, Vusumuzi Dube, Tavonga Siyavora, Subhashini Venugopalan, Jason Hicke… 展开作者

Abdoulaye Diack, Perry Nelson, Kwaku Agbesi, Angela Nakalembe, MohamedElfatih MohamedKhair, Vusumuzi Dube, Tavonga Siyavora, Subhashini Venugopalan, Jason Hickey, Uche Okonkwo, Abhishek Bapna, Isaac Wiafe, Raynard Dodzi Helegah, Elikem Doe Atsakpo, Charles Nutrokpor, Fiifi Baffoe Payin Winful, Kafui Kwashie Solaga, Jamal-Deen Abdulai, Akon Obu Ekpezu, Audace Niyonkuru, Samuel Rutunda, Boris Ishimwe, Michael Melese, Engineer Bainomugisha, Joyce Nakatumba-Nabende, Andrew Katumba, Claire Babirye, Jonathan Mukiibi, Vincent Kimani, Samuel Kibacia, James Maina, Fridah Emmah, Ahmed Ibrahim Shekarau, Ibrahim Shehu Adamu, Yusuf Abdullahi, Howard Lakougna, Bob MacDonald, Hadar Shemtov, Aisha Walcott-Bryant, Moustapha Cisse, Avinatan Hassidim, Jeff Dean, Yossi Matias

备注：: Initial dataset release with added TTS, some more to come

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AI总结：

WAXAL是一个大规模多语言非洲语音数据集，旨在通过提供24种语言的语音数据促进语音技术的公平发展和语言数字化保存。

摘要：

语音技术的进步主要偏向于资源丰富的语言，导致大多数撒哈拉以南非洲语言的使用者面临显著的数字鸿沟。为解决这一差距，我们引入了WAXAL，一个大规模、公开可访问的语音数据集，涵盖24种语言，代表超过1亿名说话者。该数据集由两个主要部分组成：包含约1250小时转录自然语音的自动语音识别（ASR）数据集，以及包含约235小时高质量单人语音录制的文本到语音（TTS）数据集，这些录制使用了语音平衡的脚本。本文详细描述了我们的数据收集、标注和质量控制方法，包括与四个非洲学术和社区组织的合作。我们提供了数据集的详细统计概述，并讨论了其潜在的局限性和伦理考虑。WAXAL数据集已发布在https://huggingface.co/datasets/google/WaxalNLP，采用宽松的CC-BY-4.0许可，以促进研究、推动包容性技术的发展，并作为这些语言数字保存的重要资源。

英文摘要：

The advancement of speech technology has predominantly favored high-resource languages, creating a significant digital divide for speakers of most Sub-Saharan African languages. To address this gap, we introduce WAXAL, a large-scale, openly accessible speech dataset for 24 languages representing over 100 million speakers. The collection consists of two main components: an Automated Speech Recognition (ASR) dataset containing approximately 1,250 hours of transcribed, natural speech from a diverse range of speakers, and a Text-to-Speech (TTS) dataset with around 235 hours of high-quality, single-speaker recordings reading phonetically balanced scripts. This paper details our methodology for data collection, annotation, and quality control, which involved partnerships with four African academic and community organizations. We provide a detailed statistical overview of the dataset and discuss its potential limitations and ethical considerations. The WAXAL datasets are released at https://huggingface.co/datasets/google/WaxalNLP under the permissive CC-BY-4.0 license to catalyze research, enable the development of inclusive technologies, and serve as a vital resource for the digital preservation of these languages.

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。