  This paper investigates the application of the probabilistic linear
discriminant analysis (PLDA) to speaker diarization of telephone conversations.
We introduce using a variational Bayes (VB) approach for inference under a PLDA
model for modeling segmental i-vectors in speaker diarization. Deterministic
annealing (DA) algorithm is imposed in order to avoid local optimal solutions
in VB iterations. We compare our proposed system with a well-known system that
applies k-means clustering on principal component analysis (PCA) coefficients
of segmental i-vectors. We used summed channel telephone data from the National
Institute of Standards and Technology (NIST) 2008 Speaker Recognition
Evaluation (SRE) as the test set in order to evaluate the performance of the
proposed system. We achieve about 20% relative improvement in Diarization Error
Rate (DER) compared to the baseline system.
