@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Kunnafonidilaw ka Cadeau: an ASR dataset of present-day Bambara
%A Leventhal, Michael
%A Diarra, Yacouba
%A Coulibaly, Nouhoum
%A Kamaté, Panga Azazia
%Y Chimoto, Everlyn Asiko
%Y Lignos, Constantine
%Y Muhammad, Shamsuddeen
%Y Abdulmumin, Idris
%Y Siro, Clemencia
%Y Adelani, David Ifeoluwa
%S Proceedings of the 7th Workshop on African Natural Language Processing (AfricaNLP 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-364-7
%F leventhal-etal-2026-kunnafonidilaw
%X 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.
%U https://aclanthology.org/2026.africanlp-main.18/
%P 190-196
Markdown (Informal)
[Kunnafonidilaw ka Cadeau: an ASR dataset of present-day Bambara](https://aclanthology.org/2026.africanlp-main.18/) (Leventhal et al., AfricaNLP 2026)
ACL
- Michael Leventhal, Yacouba Diarra, Nouhoum Coulibaly, and Panga Azazia Kamaté. 2026. Kunnafonidilaw ka Cadeau: an ASR dataset of present-day Bambara. In Proceedings of the 7th Workshop on African Natural Language Processing (AfricaNLP 2026), pages 190–196, Rabat, Morocco. Association for Computational Linguistics.