Variance-spectra based normalization for i-vector standard and probabilistic linear discriminant analysis

@inproceedings{Bousquet2012VariancespectraBN,
  title={Variance-spectra based normalization for i-vector standard and probabilistic linear discriminant analysis},
  author={Pierre-Michel Bousquet and Anthony Larcher and Driss Matrouf and Jean-François Bonastre and Oldrich Plchot},
  booktitle={Odyssey},
  year={2012}
}
I-vector extraction and Probabilistic Linear Discriminant Analysis (PLDA) has become the state-of-the-art configuration for speaker verification. Recently, Gaussian-PLDA has been improved by a preliminary length normalization of i-vectors. This normalization, known to increase the Gaussianity of the i-vector distribution, also improves performance of systems based on standard Linear Discriminant Analysis (LDA) and ”two-covariance model” scoring. But this technique follows a standardization of… CONTINUE READING
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