Discriminating Languages in a Probabilistic Latent Subspace

We explore a method to boost discriminative capabilities of Probabilistic Linear Discriminant Analysis (PLDA) model without losing its generative advantages. To this end, our focus is in a low-dimensional PLDA latent subspace. We optimize the model with respect to MMI (Maximum Mutual Information) and our own objective functions, which is an approximation to the detection cost function. We evaluate the performance on NIST Language Recognition Evaluation 2015. Our model trains faster and performs more accurately in comparison to both generative PLDA and discriminative LDA baselines with 12% and 4% relative improvement in the average detection cost, respectively. The proposed method is applicable for a broad range of closed-set tasks.