  Probabilistic linear discriminant analysis (PLDA) has broad application in
open-set verification tasks, such as speaker verification. A key concern for
PLDA is that the model is too simple (linear Gaussian) to deal with complicated
data; however, the simplicity by itself is a major advantage of PLDA, as it
leads to desirable generalization. An interesting research therefore is how to
improve modeling capacity of PLDA while retaining the simplicity. This paper
presents a decoupling approach, which involves a global model that is simple
and generalizable, and a local model that is complex and expressive. While the
global model holds a bird view on the entire data, the local model represents
the details of individual classes. We conduct a preliminary study towards this
direction and investigate a simple decoupling model including both the global
and local models. The new model, which we call decoupled PLDA, is tested on a
speaker verification task. Experimental results show that it consistently
outperforms the vanilla PLDA when the model is based on raw speaker vectors.
However, when the speaker vectors are processed by length normalization, the
advantage of decoupled PLDA will be largely lost, suggesting future research on
non-linear local models.
