no code implementations • 2 Feb 2024 • Abraham Toluwase Owodunni, Aditya Yadavalli, Chris Chinenye Emezue, Tobi Olatunji, Clinton C Mbataku
While previous approaches have focused on modeling techniques or creating accented speech datasets, gathering sufficient data for the multitude of accents, particularly in the African context, remains impractical due to their sheer diversity and associated budget constraints.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 30 Sep 2023 • Tobi Olatunji, Tejumade Afonja, Aditya Yadavalli, Chris Chinenye Emezue, Sahib Singh, Bonaventure F. P. Dossou, Joanne Osuchukwu, Salomey Osei, Atnafu Lambebo Tonja, Naome Etori, Clinton Mbataku
To our knowledge, there is no publicly available research or benchmark on accented African clinical ASR, and speech data is non-existent for the majority of African accents.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 3 Jun 2023 • Bonaventure F. P. Dossou, Atnafu Lambebo Tonja, Chris Chinenye Emezue, Tobi Olatunji, Naome A Etori, Salomey Osei, Tosin Adewumi, Sahib Singh
While there has been significant progress in ASR, African-accented clinical ASR has been understudied due to a lack of training datasets.
no code implementations • 1 Jun 2023 • Tobi Olatunji, Tejumade Afonja, Bonaventure F. P. Dossou, Atnafu Lambebo Tonja, Chris Chinenye Emezue, Amina Mardiyyah Rufai, Sahib Singh
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 artist, initiating navigation to a specific location, or documenting a laboratory result for a patient.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • EMNLP (ClinicalNLP) 2020 • Jean-Baptiste Lamare, Tobi Olatunji, Li Yao
Ample evidence suggests that better machine learning models may be steadily obtained by training on increasingly larger datasets on natural language processing (NLP) problems from non-medical domains.
no code implementations • 1 Oct 2019 • Tobi Olatunji, Li Yao
Bootstrapping labels from radiology reports has become the scalable alternative to provide inexpensive ground truth for medical imaging.
1 code implementation • 6 May 2019 • Tobi Olatunji, Li Yao, Ben Covington, Alexander Rhodes, Anthony Upton
Acquiring high-quality annotations in medical imaging is usually a costly process.
no code implementations • 1 Oct 2018 • Nithya Attaluri, Ahmed Nasir, Carolynne Powe, Harold Racz, Ben Covington, Li Yao, Jordan Prosky, Eric Poblenz, Tobi Olatunji, Kevin Lyman
Obtaining datasets labeled to facilitate model development is a challenge for most machine learning tasks.