#Update:

Kaldi now has SRE10 example., so please use the recipes in the latest kaldi. Thanks. If you need infomation on Kaldi's data format you can still refer to this document.

The subdirectories "v1" and so on are different iVector-based speaker 
 recognition recipes. The recipe in v1 demonstrates a standard approach 
 using a full-covariance GMM-UBM, iVectors, and a PLDA backend. The example 
 in v2 replaces the GMM of the v1 recipe with a time-delay deep neural 
 network.

Kaldi SRE2010

This baseline build on well-established iVector/PLDA speaker verification framework for SRE 2010 female tasks. This work is based on Kaldi SRE 2008 example. The result is reported as the clean iVector/PLDA baseline in [1] with average EER 3.50% over nine core conditions.

To run this experiment plese first check data, hardware and software requirements and then read this guide.

For information about kaldi data format and IO please read this document.

If you feel this baseline helpful kindly cite Kaldi as well as this paper:

[1] Steven Du, Xiong Xiao, Eng Siong Chng, "DNN FEATURE COMPENSATION FOR NOISE ROBUST SPEAKER VERIFICATION", ChinaSIP 2015