Papers by Mardhiyah Sanni
Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond (2025.naacl-long)
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Mardhiyah Sanni, Tassallah Abdullahi, Devendra Deepak Kayande, Emmanuel Ayodele, Naome A Etori, Michael Samwel Mollel, Moshood O. Yekini, Chibuzor Okocha, Lukman Enegi Ismaila, Folafunmi Omofoye, Boluwatife A. Adewale, Tobi Olatunji
| Challenge: | Afrispeech-Dialog is a benchmark dataset of 50 simulated medical and non-medical African-accented English conversations . a 10%+ performance degradation is found in ASR systems on long-form, accented speech . |
| Approach: | They propose to use a dataset to evaluate automatic speech recognition systems on African-accented conversations. |
| Outcome: | The proposed dataset compares state-of-the-art speech recognition systems on accented conversations with native accents and shows a 10%+ performance degradation. |
AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset (2025.acl-long)
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Charles Nimo, Tobi Olatunji, Abraham Toluwase Owodunni, Tassallah Abdullahi, Emmanuel Ayodele, Mardhiyah Sanni, Ezinwanne C. Aka, Folafunmi Omofoye, Foutse Yuehgoh, Timothy Faniran, Bonaventure F. P. Dossou, Moshood O. Yekini, Jonas Kemp, Katherine A Heller, Jude Chidubem Omeke, Chidi Asuzu Md, Naome A Etori, Aïmérou Ndiaye, Ifeoma Okoh, Evans Doe Ocansey, Wendy Kinara, Michael L. Best, Irfan Essa, Stephen Edward Moore, Chris Fourie, Mercy Nyamewaa Asiedu
| Challenge: | Recent advances in large language models (LLMs) performance on medical multiplechoice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients globally. |
| Approach: | They introduce AfriMed-QA, the first largescale Pan-African English multi-specialty medical Question-Answering (QA) dataset, with 15,000 questions sourced from over 60 medical schools across 16 countries. |
| Outcome: | The proposed model outperforms other models in the medical field and is compared with other models. |
SparseFit: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations (2024.acl-long)
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| Challenge: | Models that generate natural language explanations (NLEs) for their predictions often require large datasets of human-written NLEs at training time, which can be expensive and time-consuming to collect. |
| Approach: | They propose a sparse few-shot fine-tuning strategy that leverages discrete prompts to jointly generate predictions and NLEs. |
| Outcome: | The proposed approach compares sparse few-shot fine-tuning with existing parametric fine- tuning techniques on three sizes of the T5 language model and four datasets and produces competitive results for both task performance and NLE quality. |
AfriVox: Probing Multilingual and Accent Robustness of Speech LLMs (2026.eacl-long)
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Busayo Awobade, Mardhiyah Sanni, Tassallah Abdullahi, Chibuzor Okocha, Kelechi Ezema, Devendra Deepak Kayande, Lukman Enegi Ismaila, Tobi Olatunji, Gloria Ashiya Katuka
| Challenge: | Recent advances in multimodal and speech-native large language models have delivered impressive speech recognition, translation, understanding, and question-answering capabilities for high-resource languages. |
| Approach: | They propose to benchmark African languages and African-accented French, Arabic, and 100+ African English accents across 20 African languages. |
| Outcome: | The proposed model outperforms traditional speech transcription and translation models in African languages and non-native French or English accents. |