Uses of Class
marytts.machinelearning.GMM

Packages that use GMM

Package	Description
marytts.machinelearning	Machine learning classes for K-Means clustering, Gaussian Mixture Models, and manual data generation for testing purposes.
marytts.signalproc.adaptation.gmm.jointgmm	Joint source-target Gaussian Mixture Model based voice conversion algorithms.

Uses of GMM in marytts.machinelearning

Methods in marytts.machinelearning that return GMM

Modifier and Type	Method and Description
GMM	GMMTrainer.expectationMaximization(double[][] x, GMM initialGmm, int emMinimumIterations, int emMaximumIterations, boolean isUpdateCovariances, double tinyLogLikelihoodChangePercent, double minimumCovarianceAllowed)
GMM	GMMTrainer.train(double[][] x, GMMTrainerParams gmmParams)

Methods in marytts.machinelearning with parameters of type GMM

Modifier and Type	Method and Description
GMM	GMMTrainer.expectationMaximization(double[][] x, GMM initialGmm, int emMinimumIterations, int emMaximumIterations, boolean isUpdateCovariances, double tinyLogLikelihoodChangePercent, double minimumCovarianceAllowed)

Constructors in marytts.machinelearning with parameters of type GMM

Constructor and Description
GMM(GMM existing)
GmmDiscretizer(GMM model, boolean extraZeroClass) This constructs a Discretizer using the specified mixture model.

Uses of GMM in marytts.signalproc.adaptation.gmm.jointgmm

Fields in marytts.signalproc.adaptation.gmm.jointgmm declared as GMM

Modifier and Type	Field and Description
GMM	JointGMM.covarianceTerms
GMM	JointGMM.source
GMM	JointGMM.targetMeans

Constructors in marytts.signalproc.adaptation.gmm.jointgmm with parameters of type GMM

Constructor and Description
JointGMM(GMM gmm, FeatureFileHeader featureParamsIn)