论文标题
半监督对比度学习，并具有广义的对比损失及其对说话者认可的应用
Semi-Supervised Contrastive Learning with Generalized Contrastive Loss and Its Application to Speaker Recognition
论文作者
论文摘要
本文介绍了半监督的对比学习框架及其在与文本无关的说话者验证中的应用。拟议的框架采用了广义的对比损失（GCL）。 GCL从两个不同的学习框架中统一了损失，有监督的指标学习和无监督的对比学习，因此自然会决定半监督学习的损失。在实验中，我们将所提出的框架应用于Voxceleb数据集上的独立于文本的扬声器验证。我们证明，GCL可以以三种方式学习说话者的嵌入，有监督的学习，半监督学习和无监督的学习，而没有任何改变损失功能的定义。
This paper introduces a semi-supervised contrastive learning framework and its application to text-independent speaker verification. The proposed framework employs generalized contrastive loss (GCL). GCL unifies losses from two different learning frameworks, supervised metric learning and unsupervised contrastive learning, and thus it naturally determines the loss for semi-supervised learning. In experiments, we applied the proposed framework to text-independent speaker verification on the VoxCeleb dataset. We demonstrate that GCL enables the learning of speaker embeddings in three manners, supervised learning, semi-supervised learning, and unsupervised learning, without any changes in the definition of the loss function.
