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GLaS-JEPA: Gaussian-Regularized Speech SSL without Engineered Prediction Targets
Speech self-supervised learning aims to learn general-purpose representations for downstream speech tasks. However, current approaches rely on complex, carefully designed prediction targets. We challenge this necessity with GLaS-JEPA, a framework that directly predicts the current encoder's continuo
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