@inproceedings{leventhal-etal-2026-kunnafonidilaw,
    title = "Kunnafonidilaw ka Cadeau: an {ASR} dataset of present-day {B}ambara",
    author = "Leventhal, Michael  and
      Diarra, Yacouba  and
      Coulibaly, Nouhoum  and
      Kamat{\'e}, Panga Azazia",
    editor = "Chimoto, Everlyn Asiko  and
      Lignos, Constantine  and
      Muhammad, Shamsuddeen  and
      Abdulmumin, Idris  and
      Siro, Clemencia  and
      Adelani, David Ifeoluwa",
    booktitle = "Proceedings of the 7th Workshop on {A}frican Natural Language Processing ({A}frica{NLP} 2026)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/manual-author-scripts/2026.africanlp-main.18/",
    pages = "190--196",
    ISBN = "979-8-89176-364-7",
    abstract = "We present Kunkado, a 160-hour Bambara ASR dataset compiled from Malian radio archives to capture present-day spontaneous speech across a wide range of topics. It includes code-switching, disfluencies, background noise, and overlapping speakers that practical ASR systems encounter in real-world use. We finetuned Parakeet-based models on a 33.47-hour human-reviewed subset and apply pragmatic transcript normalization to reduce variability in number formatting, tags, and code-switching annotations. Evaluated on two real-world test sets, finetuning with Kunkado reduces WER from 44.47{\%} to 37.12{\%} on one and from 36.07{\%} to 32.33{\%} on the other. In human evaluation, the resulting model also outperforms a comparable system with the same architecture trained on 98 hours of cleaner, less realistic speech. We release the data and models to support robust ASR for predominantly oral languages."
}