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Link to original content: https://doi.org/10.48550/arXiv.2303.12582
[2303.12582] AfroDigits: A Community-Driven Spoken Digit Dataset for African Languages
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Computer Science > Computation and Language

arXiv:2303.12582 (cs)
[Submitted on 22 Mar 2023 (v1), last revised 4 Apr 2023 (this version, v2)]

Title:AfroDigits: A Community-Driven Spoken Digit Dataset for African Languages

Authors:Chris Chinenye Emezue, Sanchit Gandhi, Lewis Tunstall, Abubakar Abid, Josh Meyer, Quentin Lhoest, Pete Allen, Patrick Von Platen, Douwe Kiela, Yacine Jernite, Julien Chaumond, Merve Noyan, Omar Sanseviero
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Abstract:The advancement of speech technologies has been remarkable, yet its integration with African languages remains limited due to the scarcity of African speech corpora. To address this issue, we present AfroDigits, a minimalist, community-driven dataset of spoken digits for African languages, currently covering 38 African languages. As a demonstration of the practical applications of AfroDigits, we conduct audio digit classification experiments on six African languages [Igbo (ibo), Yoruba (yor), Rundi (run), Oshiwambo (kua), Shona (sna), and Oromo (gax)] using the Wav2Vec2.0-Large and XLS-R models. Our experiments reveal a useful insight on the effect of mixing African speech corpora during finetuning. AfroDigits is the first published audio digit dataset for African languages and we believe it will, among other things, pave the way for Afro-centric speech applications such as the recognition of telephone numbers, and street numbers. We release the dataset and platform publicly at this https URL and this https URL respectively.
Comments: Accepted to the AfricaNLP Workshop at ICLR 2023
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2303.12582 [cs.CL]
  (or arXiv:2303.12582v2 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2303.12582

Submission history

From: Chris C. Emezue [view email]
[v1] Wed, 22 Mar 2023 14:09:20 UTC (944 KB)
[v2] Tue, 4 Apr 2023 03:32:24 UTC (944 KB)
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