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

arXiv: https://arxiv.org/abs/1803.09153 · HTML: https://mlsanity.com/paper/1803.09153/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2018.

## Built on by (most cited first, 3 of 3)

- [A Speaker Verification Backend with Robust Performance across Conditions](https://mlsanity.com/paper/2102.01760/) (2021)
- [Dr-Vectors: Decision Residual Networks and an Improved Loss for Speaker Recognition](https://mlsanity.com/paper/2104.01989/) (2021)
- [Probabilistic Spherical Discriminant Analysis: An Alternative to PLDA for length-normalized embeddings](https://mlsanity.com/paper/2203.14893/) (2022)

## Abstract (arXiv)

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.