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

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