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An extension of the Gaussian mixture model is presented using Independent Component Analysis (ICA) and the generalized Gaussian density model. The mixture model assumes that the observed data can be categorized into mutually exclusive classes whose components are generated by a linear combination of independent sources. The source densities are modeled by generalized Gaussians (Box and Tiao, 19...

This paper proposes a novel technique for speaker and language adaptive training for HMM-based statistical parametric polyglot speech synthesis. Language-specific context-dependencies in the system are captured using CAT with cluster-dependent decision trees. Acoustic variations caused by speaker characteristics are handled by CMLLR-based transforms. This framework allows multi-speaker/multi-la...

We present a generalization of independent component analysis (ICA), where instead of looking for a linear transform that makes the data components independent, we look for a transform that makes the data components well fit by a tree-structured graphical model. This tree-dependent component analysis (TCA) provides a tractable and flexible approach to weakening the assumption of independence in...

Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are the techniques that deal with extracting the independent components from linear mixtures of Gaussian and non-Gaussian data at the input respectively. PCA is a classical method that deals with the second order statistics of data. It is also known as Karhunen-Loeve Transform or the Hotelling Transform in some applicat...

The increasingly popular independent component analysis (ICA) may only be applied to data following the generative ICA model in order to guarantee algorithmindependent and theoretically valid results. Subspace ICA models generalize the assumption of component independence to independence between groups of components. They are attractive candidates for dimensionality reduction methods, however a...