This recipe replaces iVectors used in the v1 recipe with embeddings extracted
   from a deep neural network.  In the scripts, we refer to these embeddings as
   "xvectors."  The recipe in local/nnet3/xvector/tuning/run_xvector_1a.sh is
   closesly based on the following paper:
  
   @inproceedings{snyder2018xvector,
   title={X-vectors: Robust DNN Embeddings for Speaker Recognition},
   author={Snyder, D. and Garcia-Romero, D. and Sell, G. and Povey, D. and Khudanpur, S.},
   booktitle={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
   year={2018},
   organization={IEEE},
   url={http://www.danielpovey.com/files/2018_icassp_xvectors.pdf}
   }
  
   The recipe uses the following data for system development.  This is in
   addition to the NIST SRE 2016 dataset used for evaluation (see ../README.txt).
   
       Corpus              LDC Catalog No.
       SWBD2 Phase 1       LDC98S75
       SWBD2 Phase 2       LDC99S79
       SWBD2 Phase 3       LDC2002S06
       SWBD Cellular 1     LDC2001S13
       SWBD Cellular 2     LDC2004S07
       SRE2004             LDC2006S44
       SRE2005 Train       LDC2011S01
       SRE2005 Test        LDC2011S04
       SRE2006 Train       LDC2011S09
       SRE2006 Test 1      LDC2011S10
       SRE2006 Test 2      LDC2012S01
       SRE2008 Train       LDC2011S05
       SRE2008 Test        LDC2011S08
       SRE2010 Eval        LDC2017S06
       Mixer 6             LDC2013S03
  
   The following datasets are used in data augmentation.
  
       MUSAN               http://www.openslr.org/17
       RIR_NOISES          http://www.openslr.org/28