Africa-Centric Self-Supervised Pre-Training for Multilingual Speech Representation in a Sub-Saharan Context
<|reference_start|>Africa-Centric Self-Supervised Pre-Training for Multilingual Speech Representation in a Sub-Saharan Context: We present the first self-supervised multilingual speech model trained exclusively on African speech. The model learned from nearly 60 000 hours of unlabeled speech segments in 21 languages and dialects spoken in sub-Saharan Africa. On the SSA subset of the FLEURS-102 dataset, our approach based on a HuBERT$_{base}$ (0.09B) architecture shows competitive results, for ASR downstream task, compared to the w2v-bert-51 (0.6B) pre-trained model proposed in the FLEURS benchmark, while being more efficient by using 7x less data and 6x less parameters. Furthermore, in the context of a LID downstream task, our approach outperforms FLEURS baselines accuracy by over 22\%.<|reference_end|>
@article{caubrière2024africa-centric,
    title={Africa-Centric Self-Supervised Pre-Training for Multilingual Speech
  Representation in a Sub-Saharan Context},
    author={Antoine Caubri`ere and Elodie Gauthier},
    journal={arXiv preprint arXiv:2404.02000},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.02000},
    primaryClass={cs.CL cs.LG cs.SD eess.AS}
}