X-Vectors: Robust DNN Embeddings for Speaker Recognition

@article{Snyder2018XVectorsRD,
  title={X-Vectors: Robust DNN Embeddings for Speaker Recognition},
  author={David Snyder and Daniel Garcia-Romero and Gregory Sell and Daniel Povey and Sanjeev Khudanpur},
  journal={2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2018},
  pages={5329-5333}
}
In this paper, we use data augmentation to improve performance of deep neural network (DNN) embeddings for speaker recognition. [] Key Method We use data augmentation, consisting of added noise and reverberation, as an inexpensive method to multiply the amount of training data and improve robustness. The x-vectors are compared with i-vector baselines on Speakers in the Wild and NIST SRE 2016 Cantonese. We find that while augmentation is beneficial in the PLDA classifier, it is not helpful in the i-vector…
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