  Bayesian HMM clustering of x-vector sequences (VBx) has become a widely
adopted diarization baseline model in publications and challenges. It uses an
HMM to model speaker turns, a generatively trained probabilistic linear
discriminant analysis (PLDA) for speaker distribution modeling, and Bayesian
inference to estimate the assignment of x-vectors to speakers. This paper
presents a new framework for updating the VBx parameters using discriminative
training, which directly optimizes a predefined loss. We also propose a new
loss that better correlates with the diarization error rate compared to binary
cross-entropy $\unicode{x2013}$ the default choice for diarization end-to-end
systems. Proof-of-concept results across three datasets (AMI, CALLHOME, and
DIHARD II) demonstrate the method's capability of automatically finding
hyperparameters, achieving comparable performance to those found by extensive
grid search, which typically requires additional hyperparameter behavior
knowledge. Moreover, we show that discriminative fine-tuning of PLDA can
further improve the model's performance. We release the source code with this
publication.
