论文标题
稳定标签分配，以通过自我监督的预训练进行语音分离
Stabilizing Label Assignment for Speech Separation by Self-supervised Pre-training
论文作者
论文摘要
语音分离已经得到很好的发展，尽管在坑训练期间，频繁的标签分配开关发生的频繁标签分配开关仍然是一个问题，这仍然是一个问题，当需要更好的收敛速度和可实现的性能时，这仍然是一个问题。在本文中，我们建议在训练语音分离模型中进行自我监督的预训练，以稳定标签分配。在几种类型的自我监督方法上，几种典型的语音分离模型和两个不同数据集的实验表明，如果选择了适当的自我监督方法，则可以实现非常好的改进。
Speech separation has been well developed, with the very successful permutation invariant training (PIT) approach, although the frequent label assignment switching happening during PIT training remains to be a problem when better convergence speed and achievable performance are desired. In this paper, we propose to perform self-supervised pre-training to stabilize the label assignment in training the speech separation model. Experiments over several types of self-supervised approaches, several typical speech separation models and two different datasets showed that very good improvements are achievable if a proper self-supervised approach is chosen.
