8 citations · 19 across the 13 of their papers we have counts for
13 papers · 1 filter
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…