Expectation Maximization Machine Learning Tools

This package is a part of Bob. It implements a general EM algorithm and includes implementations of the following algorithms:

  • K-Means

  • Maximum Likelihood (ML)

  • Maximum a Posteriori (MAP)

  • Inter Session Variability Modelling (ISV)

  • Joint Factor Analysis (JFA)

  • Total Variability Modeling (iVectors)

  • Probabilistic Linear Discriminant Analysis (PLDA)

  • EM Principal Component Analysis (EM-PCA)

References

Reynolds2000

Reynolds, Douglas A., Thomas F. Quatieri, and Robert B. Dunn. Speaker Verification Using Adapted Gaussian Mixture Models, Digital signal processing 10.1 (2000): 19-41.

Vogt2008

R. Vogt, S. Sridharan. ‘Explicit Modelling of Session Variability for Speaker Verification’, Computer Speech & Language, 2008, vol. 22, no. 1, pp. 17-38

McCool2013

C. McCool, R. Wallace, M. McLaren, L. El Shafey, S. Marcel. ‘Session Variability Modelling for Face Authentication’, IET Biometrics, 2013

ElShafey2014

Laurent El Shafey, Chris McCool, Roy Wallace, Sebastien Marcel. ‘A Scalable Formulation of Probabilistic Linear Discriminant Analysis: Applied to Face Recognition’, TPAMI’2014

PrinceElder2007

Prince and Elder. ‘Probabilistic Linear Discriminant Analysis for Inference About Identity’, ICCV’2007

LiFu2012

Li, Fu, Mohammed, Elder and Prince. ‘Probabilistic Models for Inference about Identity’, TPAMI’2012

Bishop1999

Tipping, Michael E., and Christopher M. Bishop. “Probabilistic principal component analysis.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 61.3 (1999): 611-622.

Roweis1998

Roweis, Sam. “EM algorithms for PCA and SPCA.” Advances in neural information processing systems (1998): 626-632.

Glembek2009

Glembek, Ondrej, et al. “Comparison of scoring methods used in speaker recognition with joint factor analysis.” Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on. IEEE, 2009.

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