activity
20212024
most citedLearning Nigerian accent embeddings from speech: preliminary results based on SautiDB-Naija corpus

3 citations · 7 across the 8 of their papers we have counts for

collaborators

8 papers

eess.AS2024

1000 African Voices: Advancing inclusive multi-speaker multi-accent speech synthesis

Sewade Ogun, Abraham T. Owodunni, Tobi Olatunji +6

Recent advances in speech synthesis have enabled many useful applications like audio directions in Google Maps, screen readers, and automated content generation on platforms like T…

cs.CR20242 cited

Towards Biologically Plausible and Private Gene Expression Data Generation

Dingfan Chen, Marie Oestreich, Tejumade Afonja +3

Generative models trained with Differential Privacy (DP) are becoming increasingly prominent in the creation of synthetic data for downstream applications. Existing literature, how…

cs.CL20231 cited

AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR

Tobi Olatunji, Tejumade Afonja, Aditya Yadavalli +8

Africa has a very low doctor-to-patient ratio. At very busy clinics, doctors could see 30+ patients per day -- a heavy patient burden compared with developed countries -- but produ…

cs.LG2023

MargCTGAN: A "Marginally'' Better CTGAN for the Low Sample Regime

Tejumade Afonja, Dingfan Chen, Mario Fritz

The potential of realistic and useful synthetic data is significant. However, current evaluation methods for synthetic tabular data generation predominantly focus on downstream tas…

cs.CL2023

AfriNames: Most ASR models "butcher" African Names

Tobi Olatunji, Tejumade Afonja, Bonaventure F. P. Dossou +4

Useful conversational agents must accurately capture named entities to minimize error for downstream tasks, for example, asking a voice assistant to play a track from a certain art…

cs.LG2023

Proceedings of the NeurIPS 2021 Workshop on Machine Learning for the Developing World: Global Challenges

Paula Rodriguez Diaz, Tejumade Afonja, Konstantin Klemmer +4

These are the proceedings of the 5th workshop on Machine Learning for the Developing World (ML4D), held as part of the Thirty-fifth Conference on Neural Information Processing Syst…