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arXiv · 2404.02000

Africa-Centric Self-Supervised Pre-Training for Multilingual Speech Representation in a Sub-Saharan Context

Antoine Caubrière·Elodie Gauthier
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Abstract

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\%.

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Antoine Caubrière, Elodie Gauthier. 2024-04-02. Africa-Centric Self-Supervised Pre-Training for Multilingual Speech Representation in a Sub-Saharan Context. https://arxiv.org/abs/2404.02000

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