First attempt of boltzmann machines for speaker verification

@inproceedings{Senoussaoui2012FirstAO,
  title={First attempt of boltzmann machines for speaker verification},
  author={Mohammed Senoussaoui and Najim Dehak and Patrick Kenny and R{\'e}da Dehak and Pierre Dumouchel},
  booktitle={Odyssey},
  year={2012}
}
Frequently organized by NIST, Speaker Recognition evaluations (SRE) show high accuracy rates. This demonstrates that this field of research is mature. The latest progresses came from the proposition of low dimensional i-vectors representation and new classifiers such as Probabilistic Linear Discriminant Analysis (PLDA) or Cosine Distance classifier. In this paper, we study some variants of Boltzmann Machines (BM). BM is used in image processing but still unexplored in Speaker Verification (SR… CONTINUE READING
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Key Quantitative Results

  • This new approach achieved an Equal Error Rate (EER) of 7% and a minimum Detection Cost Function (DCF) of 0.035 on the female content of the NIST SRE 2008.

Citations

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Deep belief networks for i-vector based speaker recognition

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) • 2014
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2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) • 2018
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Deep Learning Backend for Single and Multisession i-Vector Speaker Recognition

IEEE/ACM Transactions on Audio, Speech, and Language Processing • 2017
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Exploring universal speech attributes for speaker verification

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) • 2017

Evaluation of the deep nonlinear metric learning based speaker identification on the large scale of voiceprint corpus

2016 10th International Symposium on Chinese Spoken Language Processing (ISCSLP) • 2016
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