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)

• Whitening

• Within-Class Covariance Normalization (WCCN)

Documentation

• User guide
  • K-Means
  • Gaussian mixture models
  • Session Variability Modeling with Gaussian Mixture Models
• 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.

McCool2013

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

Indices and tables

• Index

• Module Index

• Search Page