自蒸馏原型网络：无监督学习鲁棒的说话人表征
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision
AI总结：
本文提出自蒸馏原型网络（SDPN），通过将话语增强视图与原始视图分配至相同原型实现自监督知识转移，并引入多样性正则化项解决模型崩溃问题，在VoxCeleb基准上达到最先进性能。
摘要：
在没有显式说话人标签的情况下训练具有说话人区分性和鲁棒性的说话人验证系统仍然是一个持续的挑战。在本文中，我们提出了一种新的自监督说话人验证方法，即自蒸馏原型网络（SDPN），该方法有效地促进了自监督说话人表征学习。SDPN将话语增强视图的表征分配给与原始视图表征相同的原型，从而实现视图之间的有效知识转移。最初，由于SDPN训练过程中缺乏负样本对，网络倾向于在嵌入空间中将正样本对非常紧密地对齐，这种现象被称为模型崩溃。为了缓解这个问题，我们在SDPN的嵌入中引入了多样性正则化项。在VoxCeleb数据集上的综合实验证明了SDPN在自监督说话人验证中的优越性。SDPN在VoxCeleb1说话人验证评估基准上创下了新的最先进水平，在训练中不使用任何说话人标签的情况下，对于测试集VoxCeleb1-O、VoxCeleb1-E和VoxCeleb1-H分别实现了1.80%、1.99%和3.62%的等错误率。
英文摘要：
Training speaker-discriminative and robust speaker verification systems without explicit speaker labels remains a persisting challenge. In this paper, we propose a new self-supervised speaker verification approach, Self-Distillation Prototypes Network (SDPN), which effectively facilitates self-supervised speaker representation learning. SDPN assigns the representation of the augmented views of an utterance to the same prototypes as the representation of the original view, thereby enabling effective knowledge transfer between the views. Originally, due to the lack of negative pairs in the SDPN training process, the network tends to align positive pairs very closely in the embedding space, a phenomenon known as model collapse. To alleviate this problem, we introduce a diversity regularization term to embeddings in SDPN. Comprehensive experiments on the VoxCeleb datasets demonstrate the superiority of SDPN in self-supervised speaker verification. SDPN sets a new state-of-the-art on the VoxCeleb1 speaker verification evaluation benchmark, achieving Equal Error Rate 1.80%, 1.99%, and 3.62% for trial VoxCeleb1-O, VoxCeleb1-E and VoxCeleb1-H respectively, without using any speaker labels in training.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
