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Multi-view feature has been proved to be very effective in many multimediaapplications. However, the current back-end classifiers cannot make full use ofsuch features. In this paper, we propose a method to model the multi-facetedinformation 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 onlyconsiders one single label. However, in practical use, when using multi-tasklearned network as feature extractor, the extracted feature are always attachedto several labels. This type of feature is called multi-view feature. Withmulti-view (joint) PLDA, we are able to explicitly build a model that cancombine multiple heterogeneous information from the multi-view features. Inverification step, we calculated the likelihood to describe whether the twofeatures having consistent labels or not. This likelihood are used in thefollowing decision-making. Experiments have been conducted on large scaleverification task. On the public RSR2015 data corpus, the results showed thatour approach can achieve 0.02\% EER and 0.09\% EER for impostor wrong andimpostor correct cases respectively.

Multi-view (Joint) Probability Linear Discrimination Analysis for Multi-view Feature Verification