Kunnafonidilaw ka Cadeau: an ASR dataset of present-day Bambara
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arXiv
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| Format: | Preprint |
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2025
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| author | Diarra, Yacouba Kamate, Panga Azazia Coulibaly, Nouhoum Souleymane Leventhal, Michael |
| author_facet | Diarra, Yacouba Kamate, Panga Azazia Coulibaly, Nouhoum Souleymane Leventhal, Michael |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19400 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Kunnafonidilaw ka Cadeau: an ASR dataset of present-day Bambara Diarra, Yacouba Kamate, Panga Azazia Coulibaly, Nouhoum Souleymane Leventhal, Michael Computation and Language 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. |
| title | Kunnafonidilaw ka Cadeau: an ASR dataset of present-day Bambara |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2512.19400 |