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The standard state-of-the-art backend for text-independent speakerrecognizers that use i-vectors or x-vectors, is Gaussian PLDA (G-PLDA),assisted by a Gaussianization step involving length normalization. G-PLDA canbe trained with both generative or discriminative methods. It has long beenknown that heavy-tailed PLDA (HT-PLDA), applied without length normalization,gives similar accuracy, but at considerable extra computational cost. We haverecently introduced a fast scoring algorithm for a discriminatively trainedHT-PLDA backend. This paper extends that work by introducing a fast,variational Bayes, generative training algorithm. We compare old and newbackends, with and without length-normalization, with i-vectors and x-vectors,on SRE'10, SRE'16 and SITW.

Fast variational Bayes for heavy-tailed PLDA applied to i-vectors and x-vectors