  In this paper, we analyze the behavior and performance of speaker embeddings
and the back-end scoring model under domain and language mismatch. We present
our findings regarding ResNet-based speaker embedding architectures and show
that reduced temporal stride yields improved performance. We then consider a
PLDA back-end and show how a combination of small speaker subspace,
language-dependent PLDA mixture, and nuisance-attribute projection can have a
drastic impact on the performance of the system. Besides, we present an
efficient way of scoring and fusing class posterior logit vectors recently
shown to perform well for speaker verification task. The experiments are
performed using the NIST SRE 2021 setup.
