Contextual Additive Structure for HMM-Based Speech Synthesis

This paper proposes a spectral modeling technique based on an additive structure of context dependencies for HMM-based speech synthesis. Contextual additive structure models can represent complicated dependencies between acoustic features and context labels using multiple decision trees. However, the computational complexity of the context clustering is too high for the full context labels of speech synthesis. To overcome this problem, this paper proposes two approaches; covariance parameter tying and a likelihood calculation algorithm using the matrix inversion lemma. Additive structure models can be applied to HMM-based speech synthesis using these techniques and speech quality would significantly be improved. Experimental results show that the proposed method outperforms the conventional one in subjective listening tests.

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