  Speaker embedding extractors are typically trained using a classification
loss over the training speakers. During the last few years, the standard
softmax/cross-entropy loss has been replaced by the margin-based losses,
yielding significant improvements in speaker recognition accuracy. Motivated by
the fact that the margin merely reduces the logit of the target speaker during
training, we consider a probabilistic framework that has a similar effect. The
variational information bottleneck provides a principled mechanism for making
deterministic nodes stochastic, resulting in an implicit reduction of the
posterior of the target speaker. We experiment with a wide range of speaker
recognition benchmarks and scoring methods and report competitive results to
those obtained with the state-of-the-art Additive Angular Margin loss.
