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)

Documentation

• User guide
  • K-Means
  • Gaussian mixture models
  • Session Variability Modeling with Gaussian Mixture Models
  • Probabilistic Linear Discriminant Analysis (PLDA)
  • Score Normalization
• Python API
  • Classes
  • Functions
  • Detailed Information

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.

[Auckenthaler2000]

Auckenthaler, Roland, Michael Carey, and Harvey Lloyd-Thomas. “Score normalization for text-independent speaker verification systems.” Digital Signal Processing 10.1 (2000): 42-54.

[Mariethoz2005]

Mariethoz, Johnny, and Samy Bengio. “A unified framework for score normalization techniques applied to text-independent speaker verification.” IEEE signal processing letters 12.7 (2005): 532-535.

Indices and tables

• Index
• Module Index
• Search Page