  Many modern systems for speaker diarization, such as the recently-developed
VBx approach, rely on clustering of DNN speaker embeddings followed by
resegmentation. Two problems with this approach are that the DNN is not
directly optimized for this task, and the parameters need significant retuning
for different applications. We have recently presented progress in this
direction with a Leave-One-Out Gaussian PLDA (LGP) clustering algorithm and an
approach to training the DNN such that embeddings directly optimize performance
of this scoring method. This paper presents a new two-pass version of this
system, where the second pass uses finer time resolution to significantly
improve overall performance. For the Callhome corpus, we achieve the first
published error rate below 4% without any task-dependent parameter tuning. We
also show significant progress towards a robust single solution for multiple
diarization tasks.
