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Learning Nigerian accent embeddings from speech: preliminary results based on SautiDB-Naija corpus

2021-12-12 10:50:01
Tejumade Afonja, Oladimeji Mudele, Iroro Orife, Kenechi Dukor, Lawrence Francis, Duru Goodness, Oluwafemi Azeez, Ademola Malomo, Clinton Mbataku
arXiv_CL
       
Abstract

This paper describes foundational efforts with SautiDB-Naija, a novel corpus of non-native (L2) Nigerian English speech. We describe how the corpus was created and curated as well as preliminary experiments with accent classification and learning Nigerian accent embeddings. The initial version of the corpus includes over 900 recordings from L2 English speakers of Nigerian languages, such as Yoruba, Igbo, Edo, Efik-Ibibio, and Igala. We further demonstrate how fine-tuning on a pre-trained model like wav2vec can yield representations suitable for related speech tasks such as accent classification. SautiDB-Naija has been published to Zenodo for general use under a flexible Creative Commons License.

Abstract (translated)

URL

https://arxiv.org/abs/2112.06199

PDF

https://arxiv.org/pdf/2112.06199.pdf

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