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Contextual partial additive structure for HMM-based speech synthesis

This paper proposes a spectral modeling technique based on a contextual partial additive structure for HMM-based speech synthesis. To represent complicated context dependencies, contextual additive structure models assume multiple independent components which have different context dependencies to form acoustic features. In additive structure models, there is a constraint that a fixed number of additive components are used for generating acoustic features. However, it is natural to assume that the number of components depends on contexts. In the proposed technique, partial additive components affecting arbitrary contextual sub-spaces are created on demand to increase the likelihood. Then, the number of components for each context can be automatically determined with the training data. Experimental results show that the proposed technique outperformed the standard technique in a subjective test.