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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...
This paper presents a method to improve hidden Markov model (HMM) based parametric speech synthesis by integrating global variance (GV) of log power spectrum (LPS) derived from line spectral pairs (LSPs) into minimum generation error (MGE) model training. In order to alleviate the over-smoothing effect of the generated spectral structures, an LPS-GV based parameter generation method has been proposed...
This paper describes a new approach which utilizes neural autoregressive distribution estimators (NADE) for the spectral modeling in statistical parametric speech synthesis. In order to alleviate the over-smoothing effect on the generated spectral structures, a restricted Boltzmann machine (RBM) modeling method has been proposed in our previous work, where the RBM is adopted to represent the joint...
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