For speaker recognition, it is difficult to extract an accurate speaker
representation from speech because of its mixture of speaker traits and
content. This paper proposes a disentanglement framework that simultaneously
models speaker traits and content variability in speech. It is realized with
the use of three Gaussian inference layers, each consisting of a learnable
transition model that extracts distinct speech components. Notably, a
strengthened transition model is specifically designed to model complex speech
dynamics. We also propose a self-supervision method to dynamically disentangle
content without the use of labels other than speaker identities. The efficacy
of the proposed framework is validated via experiments conducted on the
VoxCeleb and SITW datasets with 9.56% and 8.24% average reductions in EER and
minDCF, respectively. Since neither additional model training nor data is
specifically needed, it is easily applicable in practical use.

该研究提出了一种能同时模拟语音中的说话人特征和内容可变性的解缠结构框架，并通过使用三个高斯推理层实现，其中每个层由可学习的转换模型组成，能够提取出独特的语音组成成分。通过在 VoxCeleb 和 SITW 数据集上进行实验证实了该框架的有效性，其表现为等价错误率和最小 DCF 分别降低了 9.56％和 8.24％，而不需要额外的模型训练或数据，因此可以在实际应用中容易使用。