Joint PLDA for Simultaneous Modeling of Two Factors
This work addresses speaker verification challenges in multilingual settings, offering an incremental improvement to existing PLDA methods.
The authors tackled the problem of modeling both class identity and nuisance conditions in biometric recognition by proposing a joint PLDA approach, which achieved significant performance gains in multilingual speaker verification, particularly with monolingual training data.
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.