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

## TL;DR

This paper introduces a fast variational Bayes training algorithm for heavy-tailed PLDA, improving speaker recognition backend efficiency for i-vectors and x-vectors without length normalization.

## Contribution

It presents a novel, fast variational Bayes method for generative training of heavy-tailed PLDA, enhancing computational efficiency over previous approaches.

## Key findings

- Heavy-tailed PLDA matches Gaussian PLDA accuracy without length normalization.
- The new variational Bayes training reduces computational costs.
- Experimental results on SRE and SITW datasets demonstrate improved performance.

## 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.