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arXiv · 1803.09153

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

Anna Silnova·Niko Brummer·Daniel Garcia-Romero·David Snyder·Lukas Burget
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Abstract

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

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Anna Silnova, Niko Brummer, Daniel Garcia-Romero, David Snyder, Lukas Burget. 2018-03-24. Fast variational Bayes for heavy-tailed PLDA applied to i-vectors and x-vectors. https://arxiv.org/abs/1803.09153

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