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This paper proposes a spectral modeling method using a deep conditional restricted Boltzmann machine (DCRBM) for statistical parametric speech synthesis. In this method, a DCRBM, which combines a deep neural network (DNN) with a conditional restricted Boltzmann machine (CRBM), is utilized to describe the conditional distribution of spectral envelopes given linguistic features. Compared with DNN and...
This paper investigates F0 modeling of speech in deep neural networks (DNN) for statistical parametric speech synthesis (SPSS). Recently, DNN has been applied to the acoustic modeling of SPSS and has shown good performance in characterizing complex dependencies between contextual features and acoustic observations. However, the additive nature and long-term suprasegmental property of F0 features have...
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