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)




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.


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


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


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


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


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


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.


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


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.

Indices and tables