Deep neural network-based speaker embeddings for end-to-end speaker verification

@article{Snyder2016DeepNN,
  title={Deep neural network-based speaker embeddings for end-to-end speaker verification},
  author={David Snyder and Pegah Ghahremani and Daniel Povey and Daniel Garcia-Romero and Yishay Carmiel and Sanjeev Khudanpur},
  journal={2016 IEEE Spoken Language Technology Workshop (SLT)},
  year={2016},
  pages={165-170}
}
In this study, we investigate an end-to-end text-independent speaker verification system. The architecture consists of a deep neural network that takes a variable length speech segment and maps it to a speaker embedding. The objective function separates same-speaker and different-speaker pairs, and is reused during verification. Similar systems have recently shown promise for text-dependent verification, but we believe that this is unexplored for the text-independent task. We show that given a… CONTINUE READING
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