Combining Cohort and UBM Models in Open Set Speaker Identification

In open set speaker identification it is important to build an alternative model against which to compare scores from the 'target' speaker model. Two alternative strategies for building an alternative model are to build a single global model by sampling from a pool of training data, the Universal Background  (UBM), or to build a cohort of models from selected individuals in the training data for the target speaker. The main contribution in this paper is to show that these approaches can be unified by using a Support Vector Machine (SVM) to learn a decision rule in the score space made up of the output scores of the client, cohort and UBM model.