Learning layer weights on continuous features and reusing them for discrete token extraction, plus fine-tuning XLS-R with extra data, yields 44% relative CER reduction on ML-SUPERB and tops the challenge's single-system leaderboard.
IEEE Journal of Selected Topics in Signal Processing 11(8), 1240–1253 (2017) 5 https://huggingface.co/microsoft/wavlm-large 8 Z
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Multilingual Speech Recognition Using Discrete Tokens with a Two-step Training Strategy
Learning layer weights on continuous features and reusing them for discrete token extraction, plus fine-tuning XLS-R with extra data, yields 44% relative CER reduction on ML-SUPERB and tops the challenge's single-system leaderboard.