  Domain mismatch problem caused by speaker-unrelated feature has been a major
topic in speaker recognition. In this paper, we propose an explicit
disentanglement framework to unravel speaker-relevant features from
speaker-unrelated features via mutual information (MI) minimization. To achieve
our goal of minimizing MI between speaker-related and speaker-unrelated
features, we adopt a contrastive log-ratio upper bound (CLUB), which exploits
the upper bound of MI. Our framework is constructed in a 3-stage structure.
First, in the front-end encoder, input speech is encoded into shared initial
embedding. Next, in the decoupling block, shared initial embedding is split
into separate speaker-related and speaker-unrelated embeddings. Finally,
disentanglement is conducted by MI minimization in the last stage. Experiments
on Far-Field Speaker Verification Challenge 2022 (FFSVC2022) demonstrate that
our proposed framework is effective for disentanglement. Also, to utilize
domain-unknown datasets containing numerous speakers, we pre-trained the
front-end encoder with VoxCeleb datasets. We then fine-tuned the speaker
embedding model in the disentanglement framework with FFSVC 2022 dataset. The
experimental results show that fine-tuning with a disentanglement framework on
a existing pre-trained model is valid and can further improve performance.
