We introduce SALAN, a large-scale speech dataset covering eight of the major indigenous languages of Niger: Zarma, Hausa, Buduma, Gourmantchema, Tubu, Tamasheq, Fulfulde, and Kanuri. The final dataset exceeds 2,000 hours of audio, largely sourced from radio broadcasts and community recordings. We transcribed portions of the audio using the MMS model and conducted manual verification for 110 hours across Zarma and Hausa. We then used active learning to expand annotation to an additional 5 hours of high-uncertainty Zarma segments. To evaluate SALAN’s utility for ASR, We fine-tuned both Wav2vec2 XLS-R and Whisper on Zarma subsets and carried out additional pre-training with multilingual unlabeled data. Our best model achieved a word error rate of 25.3% and a character error rate of 6.2%. SALAN and the trained models will be made publicly available for use by researchers and speakers, with the potential to impact over 20 million individuals in Niger and neighboring countries.

Orange Silicon Valley hosted a low-resource machine translation (MT) competition with monetary prizes. The goals of the competition were to raise awareness of the challenges in the low-resource MT domain, improve MT algorithms and data strategies, and support MT expertise development in the regions where people speak Bambara and other low-resource languages. The participants built Bambara to French and French to Bambara machine translation systems using data provided by the organizers and additional data resources shared amongst the competitors. This paper details each team’s different approaches and motivation for ongoing work in Bambara and the broader low-resource machine translation domain.