Conference paper

SILNOVA Anna, BRUMMER Niko, GARCÍA-ROMERO Daniel, SNYDER David and BURGET Lukáš. Fast variational Bayes for heavy-tailed PLDA applied to i-vectors and x-vectors. In: Proceedings of Interspeech 2018. Hyderabad: International Speech Communication Association, 2018, pp. 72-76. ISSN 1990-9772. Available from:
Publication language:english
Original title:Fast variational Bayes for heavy-tailed PLDA applied to i-vectors and x-vectors
Title (cs):Rychlý variační Bayes pro PLDA model s těžkým chvostem aplikovaný na i-vektory a x-vektory
Proceedings:Proceedings of Interspeech 2018
Conference:Interspeech 2018
Place:Hyderabad, IN
Journal:Proceedings of Interspeech, Vol. 2018, No. 9, FR
Publisher:International Speech Communication Association
peaker recognition, variational Bayes, heavytailed PLDA
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 SRE10, SRE16 and SITW.
   author = {Anna Silnova and Niko Brummer and Daniel
	Garc{\'{i}}a-Romero and David Snyder and
	Luk{\'{a}}{\v{s}} Burget},
   title = {Fast variational Bayes for heavy-tailed PLDA
	applied to i-vectors and x-vectors},
   pages = {72--76},
   booktitle = {Proceedings of Interspeech 2018},
   journal = {Proceedings of Interspeech},
   volume = 2018,
 number = 9,
   year = 2018,
   location = {Hyderabad, IN},
   publisher = {International Speech Communication Association},
   ISSN = {1990-9772},
   doi = {10.21437/Interspeech.2018-2128},
   language = {english},
   url = {}

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