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WAXAL: A Large-Scale Multilingual African Language Speech Corpus

arXiv – CS AI|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|March 3, 2026 at 05:00 AM|3 views

🤖AI Summary

Researchers have released WAXAL, a large-scale multilingual speech dataset covering 24 Sub-Saharan African languages representing over 100 million speakers. The dataset includes 1,250 hours of transcribed speech for ASR and 235 hours of high-quality recordings for TTS, released under CC-BY-4.0 license to advance inclusive AI technologies.

Key Takeaways

→WAXAL addresses the digital divide by providing speech data for 24 underrepresented Sub-Saharan African languages.
→The dataset contains approximately 1,250 hours of ASR data and 235 hours of TTS data from diverse speakers.
→The corpus was developed through partnerships with four African academic and community organizations.
→All data is openly accessible under the permissive CC-BY-4.0 license on Hugging Face.
→The release aims to catalyze research in inclusive AI technologies and digital language preservation.