4 accepted papers
This paper proposes a novel framework of training method of deep Gaussian processes (DGPs). DGPs are deep architecture models based on stacked multiple GPs, which can overcome the limitation of single-layer GPs. Although a stochastic variational inference (SVI)- based method has been proposed for DG…
This paper proposes an alternative multi-level approach to duration prediction for improving prosody generation in statistical parametric speech synthesis using multiple Gaussian process experts. We use two duration models at different levels, specifically, syllable and phone. First, we individually…
In this paper, we propose a speaker adaptation technique for statistical parametric speech synthesis based on Gaussian process regression (GPR). Although it is reported that the GPR-based speech synthesis improves the naturalness of synthetic speech compared with the HMM-based speech synthesis, any…
This paper proposes novel models of F0 contours and phone durations using Gaussian process regression and classification (GPR and GPC) for statistical parametric speech synthesis. Although the use of frame-based GPR has shown the effectiveness of spectral feature modeling in previous studies, the ap…