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

• DOI: 10.48550/arxiv.2112.06199
• Publication Date: 2021-01-01
• Authors: Afonja, Tejumade; Mudele, Oladimeji; Orife, Iroro; Dukor, Kenechi; Francis, Lawrence; Goodness, Duru; Azeez, Oluwafemi; Malomo, Ademola; Mbataku, Clinton
• Keywords: FOS: Computer and information sciences; 03 medical and health sciences; Sound (cs.SD); Computer Science - Computation and Language; Audio and Speech Processing (eess.AS); 0202 electrical engineering, electronic engineering, information engineering; FOS: Electrical engineering, electronic engineering, information engineering; 02 engineering and technology; 0305 other medical science; Computation and Language (cs.CL); Computer Science - Sound; Electrical Engineering and Systems Science - Audio and Speech Processing

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.