Probablistic Linear Discriminant Analysis of i–Vector Posterior Distributions – Sandro Cumani (Brno University of Technology)
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The i–vector extraction process is characterized by an intrinsic uncertainty represented by the i–vector posterior covariance. The usual PLDA models, however, ignore such uncertainty and perform speaker inference based only on point estimates of the i–vector distributions. We therefore propose a new PLDA model which takes into account the i–vector uncertainty. Since utterance length is the main factor affecting i–vector covariances, we designed a set of experiments to compare the proposed model and the classical PLDA model over segments with short and missmatching durations. The results show that the proposed model allows to improve the accuracy on short segments while retainig the accuracy of the original PLDA over long utterances.