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- W4387560097 abstract "The 2023 Multilingual Speech Universal Performance Benchmark (ML-SUPERB) Challenge expands upon the acclaimed SUPERB framework, emphasizing self-supervised models in multilingual speech recognition and language identification. The challenge comprises a research track focused on applying ML-SUPERB to specific multilingual subjects, a Challenge Track for model submissions, and a New Language Track where language resource researchers can contribute and evaluate their low-resource language data in the context of the latest progress in multilingual speech recognition. The challenge garnered 12 model submissions and 54 language corpora, resulting in a comprehensive benchmark encompassing 154 languages. The findings indicate that merely scaling models is not the definitive solution for multilingual speech tasks, and a variety of speech/voice types present significant challenges in multilingual speech processing." @default.
- W4387560097 created "2023-10-12" @default.
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- W4387560097 date "2023-10-09" @default.
- W4387560097 modified "2023-10-18" @default.
- W4387560097 title "Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyond" @default.
- W4387560097 doi "https://doi.org/10.48550/arxiv.2310.05513" @default.
- W4387560097 hasPublicationYear "2023" @default.
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