Kunnafonidilaw ka Cadeau: an ASR dataset of present-day Bambara

• URL: http://arxiv.org/abs/2512.19400v1
• Date: Mon, 22 Dec 2025 13:52:33 GMT
• Title: Kunnafonidilaw ka Cadeau: an ASR dataset of present-day Bambara
• Authors: Yacouba Diarra, Panga Azazia Kamate, Nouhoum Souleymane Coulibaly, Michael Leventhal,
• Abstract summary: Kunkado is a 160-hour Bambara ASR dataset compiled from Malian radio archives.<n>It includes code-switching, disfluencies, background noise, and overlapping speakers that practical ASR systems encounter in real-world use.
• Score: 0.7999703756441755
• License: http://creativecommons.org/licenses/by/4.0/
• 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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