  Self-supervised learning (SSL) is a long-standing goal for speech processing,
since it utilizes large-scale unlabeled data and avoids extensive human
labeling. Recent years witness great successes in applying self-supervised
learning in speech recognition, while limited exploration was attempted in
applying SSL for modeling speaker characteristics. In this paper, we aim to
improve the existing SSL framework for speaker representation learning. Two
methods are introduced for enhancing the unsupervised speaker information
extraction. First, we apply the multi-task learning to 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.
