  Multi-view feature has been proved to be very effective in many multimedia
applications. However, the current back-end classifiers cannot make full use of
such features. In this paper, we propose a method to model the multi-faceted
information in the multi-view features explicitly and jointly. In our approach,
the feature was modeled as a result derived by a generative multi-view
(joint\footnotemark[1]) Probability Linear Discriminant Analysis (PLDA) model,
which contains multiple kinds of latent variables. The usual PLDA model only
considers one single label. However, in practical use, when using multi-task
learned network as feature extractor, the extracted feature are always attached
to several labels. This type of feature is called multi-view feature. With
multi-view (joint) PLDA, we are able to explicitly build a model that can
combine multiple heterogeneous information from the multi-view features. In
verification step, we calculated the likelihood to describe whether the two
features having consistent labels or not. This likelihood are used in the
following decision-making. Experiments have been conducted on large scale
verification task. On the public RSR2015 data corpus, the results showed that
our approach can achieve 0.02\% EER and 0.09\% EER for impostor wrong and
impostor correct cases respectively.
