the current SSL framework, where we integrate the utterance-wise contrastive loss with the SSL objective function.
Second, for better speaker discrimination, we propose an utterance mixing strategy for data augmentation, where
additional overlapped utterances are created unsupervisely and incorporate during training. We integrate the proposed
methods into the HuBERT framework. Experiment results on SUPERB benchmark show that the proposed system achieves
state-of-the-art performance in universal representation learning, especially for speaker identification oriented
tasks. An ablation study is performed verifying the efficacy of each proposed method. Finally, we scale up training
dataset to 94 thousand hours public audio data and achieve further performance improvement in all SUPERB tasks.
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