@inproceedings{sanni-etal-2025-afrispeech,
    title = "Afrispeech-Dialog: A Benchmark Dataset for Spontaneous {E}nglish Conversations in Healthcare and Beyond",
    author = "Sanni, Mardhiyah  and
      Abdullahi, Tassallah  and
      Kayande, Devendra Deepak  and
      Ayodele, Emmanuel  and
      Etori, Naome A  and
      Mollel, Michael Samwel  and
      Yekini, Moshood O.  and
      Okocha, Chibuzor  and
      Ismaila, Lukman Enegi  and
      Omofoye, Folafunmi  and
      Adewale, Boluwatife A.  and
      Olatunji, Tobi",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.naacl-long.426/",
    pages = "8399--8417",
    ISBN = "979-8-89176-189-6",
    abstract = "Speech technologies are transforming interactions across various sectors, from healthcare to call centers and robots, yet their performance on African-accented conversations remains underexplored. We introduce Afrispeech-Dialog, a benchmark dataset of 50 simulated medical and non-medical African-accented English conversations, designed to evaluate automatic speech recognition (ASR) and related technologies. We assess state-of-the-art (SOTA) speaker diarization and ASR systems on long-form, accented speech, comparing their performance with native accents and discover a 10{\%}+ performance degradation. Additionally, we explore medical conversation summarization capabilities of large language models (LLMs) to demonstrate the impact of ASR errors on downstream medical summaries, providing insights into the challenges and opportunities for speech technologies in the Global South. Our work highlights the need for more inclusive datasets to advance conversational AI in low-resource settings."
}