| Package | Description |
|---|---|
| marytts.machinelearning |
Machine learning classes for K-Means clustering, Gaussian Mixture
Models, and manual data generation for testing purposes.
|
| marytts.modules |
All the modules doing the actual processing.
|
| marytts.signalproc.adaptation.gmm.jointgmm |
Joint source-target Gaussian Mixture Model based voice conversion algorithms.
|
| Class and Description |
|---|
| Cluster
Implements a cluster center that has a mean vector and a covariance matrix (and its inverse)
|
| ContextualGMMParams
Wrapper for contextual parameters for GMM training - includes various phone identity or class based groups
|
| Discretizer |
| GaussianComponent
Implements a single Gaussian component with a mean vector and a covariance matrix It also computes terms for pdf computation
out of this Gaussian component once the mean and covariance is specified
|
| GMM
Wrapper for a Gaussian Mixture Model
|
| GmmDiscretizer
This discretizes values according to a gaussian mixture model (gmm).
|
| GMMTrainerParams
Wrapper class for GMM training parameters
|
| KMeansClusteringTrainer
K-Means clustering training algorithm
Reference: J.
|
| KMeansClusteringTrainerParams
Wrapper class for K-Means clustering training parameters
|
| PolynomialCluster
Implements a cluster center that has a mean
|
| SoP
Contains the coefficients and factors of an equation of the form: if interceptTterm = TRUE solution = coeffs[0] +
coeffs[1]*factors[0] + coeffs[2]*factors[1] + ...
|
| Class and Description |
|---|
| SoP
Contains the coefficients and factors of an equation of the form: if interceptTterm = TRUE solution = coeffs[0] +
coeffs[1]*factors[0] + coeffs[2]*factors[1] + ...
|
| Class and Description |
|---|
| ContextualGMMParams
Wrapper for contextual parameters for GMM training - includes various phone identity or class based groups
|
| GMM
Wrapper for a Gaussian Mixture Model
|
| GMMTrainerParams
Wrapper class for GMM training parameters
|
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