  Learning robust speaker embeddings is a crucial step in speaker diarization.
Deep neural networks can accurately capture speaker discriminative
characteristics and popular deep embeddings such as x-vectors are nowadays a
fundamental component of modern diarization systems. Recently, some
improvements over the standard TDNN architecture used for x-vectors have been
proposed. The ECAPA-TDNN model, for instance, has shown impressive performance
in the speaker verification domain, thanks to a carefully designed neural
model.
  In this work, we extend, for the first time, the use of the ECAPA-TDNN model
to speaker diarization. Moreover, we improved its robustness with a powerful
augmentation scheme that concatenates several contaminated versions of the same
signal within the same training batch. The ECAPA-TDNN model turned out to
provide robust speaker embeddings under both close-talking and distant-talking
conditions. Our results on the popular AMI meeting corpus show that our system
significantly outperforms recently proposed approaches.
