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Evaluating Self-Supervised Speech Representations for Indigenous American Languages
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Evaluating Self-Supervised Speech Representations for Indigenous American Languages
The application of self-supervision to speech representation learning has garnered significant interest in recent years, due to its scalability to large amounts of unlabeled data. However, much progress, both in terms of pre-training and downstream evaluation, has remained concentrated in monolingual models that only consider English. Few models consider other languages, and even fewer consider indigenous ones. In this work, benchmark the efficacy of large SSL models on 6 indigenous America languages: Quechua, Guarani , Bribri, Kotiria, Wa’ikhana, and Totonac on low-resource ASR. Our results show surprisingly strong performance by state-of-the-art SSL models, showing the potential generalizability of large-scale models to real-world data.
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