Parametric speech synthesis based on Gaussian process regression using global variance and hyperparameter optimization

Abstract

This paper examines two issues of a statistical speech synthesis approach based Gaussian process (GP) regression. Although GP-based speech synthesis can give higher performance in generating spectral parameters than the HMM-based one, a number of issues still remain. In this paper, we incorporate global variance (GV) feature to overcome over-smoothing problem into the parameter generation. Furthermore, in order to utilize an appropriate kernel function in accordance with actual data, we propose an EM-based kernel hyperparameter optimization technique. Objective and subjective evaluation results show that using GV and hyperparameter estimation enhanced the performance in spectral feature generation.