ISCA Archive Odyssey 2014
ISCA Archive Odyssey 2014

Exploring some limits of Gaussian PLDA modeling for i-vector distributions

Pierre-Michel Bousquet, Jean-Fran├žois Bonastre, Driss Matrouf

Gaussian-PLDA (G-PLDA) modeling for i-vector based speaker verification has proven to be competitive versus heavy-tailed PLDA (HT-PLDA) based on Student's t-distribution, when the latter is much more computationally expensive. However, its results are achieved using a length-normalization, which projects i-vectors on the non-linear and finite surface of a hypersphere. This paper investigates the limits of linear and Gaussian G-PLDA modeling when distribution of data is spherical. In particular, assumptions of homoscedasticity are questionable: the model assumes that the within-speaker variability can be estimated by a unique and linear parameter. A non-probabilistic approach is proposed, competitive with state-of-the-art, which reveals some limits of the Gaussian modeling in terms of goodness of fit. We carry out an analysis of residue, which finds out a relation between the dispersion of a speaker-class and its location and, thus, shows that homoscedasticity assumptions are not fulfilled.


doi: 10.21437/Odyssey.2014-7

Cite as: Bousquet, P.-M., Bonastre, J.-F., Matrouf, D. (2014) Exploring some limits of Gaussian PLDA modeling for i-vector distributions. Proc. The Speaker and Language Recognition Workshop (Odyssey 2014), 41-47, doi: 10.21437/Odyssey.2014-7

@inproceedings{bousquet14_odyssey,
  author={Pierre-Michel Bousquet and Jean-Fran├žois Bonastre and Driss Matrouf},
  title={{Exploring some limits of Gaussian PLDA modeling  for i-vector distributions}},
  year=2014,
  booktitle={Proc. The Speaker and Language Recognition Workshop (Odyssey 2014)},
  pages={41--47},
  doi={10.21437/Odyssey.2014-7}
}