  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.
