8 citations · 22 across the 14 of their papers we have counts for
15 papers
AfriSwitch: A Benchmark for In-the-Wild African Code-Switched Speech Recognition
Gabrial Zencha Ashungafac, Busayo Awobade, Tobi Olatunji
Code-switching is pervasive in bilingual African conversation, yet most ASR systems assume monolingual input and are evaluated on curated monolingual benchmarks. We present AfriSwi…
AfriVox-v2: A Domain-Verticalized Benchmark for In-the-Wild African Speech Recognition
Busayo Awobade, Gabrial Zencha Ashungafac, Tobi Olatunji
Recent large language models (LLMs) show strong speech recognition and translation capabilities for high-resource languages. However, African languages remain dramatically underrep…
AfriSpeech-MultiBench: A Verticalized Multidomain Multicountry Benchmark Suite for African Accented English ASR
Gabrial Zencha Ashungafac, Mardhiyah Sanni, Busayo Awobade +2
Recent advances in speech-enabled AI, including Google's NotebookLM and OpenAI's speech-to-speech API, are driving widespread interest in voice interfaces globally. Despite this mo…
Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond
Mardhiyah Sanni, Tassallah Abdullahi, Devendra D. Kayande +9
Speech technologies are transforming interactions across various sectors, from healthcare to call centers and robots, yet their performance on African-accented conversations remain…
The Multicultural Medical Assistant: Can LLMs Improve Medical ASR Errors Across Borders?
Ayo Adedeji, Mardhiyah Sanni, Emmanuel Ayodele +2
The global adoption of Large Language Models (LLMs) in healthcare shows promise to enhance clinical workflows and improve patient outcomes. However, Automatic Speech Recognition (A…
AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset
Tobi Olatunji, Charles Nimo, Abraham Owodunni +23
Recent advancements in large language model(LLM) performance on medical multiple choice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients gl…
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…
Performant ASR Models for Medical Entities in Accented Speech
Tejumade Afonja, Tobi Olatunji, Sewade Ogun +3
Recent strides in automatic speech recognition (ASR) have accelerated their application in the medical domain where their performance on accented medical named entities (NE) such a…
AccentFold: A Journey through African Accents for Zero-Shot ASR Adaptation to Target Accents
Abraham Toluwase Owodunni, Aditya Yadavalli, Chris Chinenye Emezue +2
Despite advancements in speech recognition, accented speech remains challenging. While previous approaches have focused on modeling techniques or creating accented speech datasets,…
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…
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…
On the diminishing return of labeling clinical reports
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…
Learning to estimate label uncertainty for automatic radiology report parsing
Tobi Olatunji, Li Yao
Bootstrapping labels from radiology reports has become the scalable alternative to provide inexpensive ground truth for medical imaging. Because of the domain specific nature, stat…
Caveats in Generating Medical Imaging Labels from Radiology Reports
Tobi Olatunji, Li Yao, Ben Covington +2
Acquiring high-quality annotations in medical imaging is usually a costly process. Automatic label extraction with natural language processing (NLP) has emerged as a promising work…
Efficient and Accurate Abnormality Mining from Radiology Reports with Customized False Positive Reduction
Nithya Attaluri, Ahmed Nasir, Carolynne Powe +7
Obtaining datasets labeled to facilitate model development is a challenge for most machine learning tasks. The difficulty is heightened for medical imaging, where data itself is li…