  Probabilistic linear discriminant analysis (PLDA) is a method used for
biometric problems like speaker or face recognition that models the variability
of the samples using two latent variables, one that depends on the class of the
sample and another one that is assumed independent across samples and models
the within-class variability. In this work, we propose a generalization of PLDA
that enables joint modeling of two sample-dependent factors: the class of
interest and a nuisance condition. The approach does not change the basic form
of PLDA but rather modifies the training procedure to consider the dependency
across samples of the latent variable that models within-class variability.
While the identity of the nuisance condition is needed during training, it is
not needed during testing since we propose a scoring procedure that
marginalizes over the corresponding latent variable. We show results on a
multilingual speaker-verification task, where the language spoken is considered
a nuisance condition. We show that the proposed joint PLDA approach leads to
significant performance gains in this task for two different datasets, in
particular when the training data contains mostly or only monolingual speakers.
