Excitation modelling using epoch features for statistical parametric speech synthesis

Abstract

In this paper, a novel excitation modelling method is proposed for improving the naturalness of statistical parametric speech synthesis (SPSS). In the proposed approach, the excitation or residual signal is parameterized by using features extracted from the epochs. The epoch parameters used in this work are epoch strength and sharpness. These features are modeled in the statistical framework along with other parameters. During synthesis, the excitation signal is constructed by imposing the generated epoch parameters on the natural instances of excitation signal. The effectiveness of the proposed method is evaluated in the framework of hidden Markov model (HMM)-based and deep neural network (DNN)-based SPSS. Evaluation results have shown that the SPSS systems developed using the proposed excitation model are capable of synthesizing more natural sounding speech compared to the ones based on two state-of-the-art excitation modelling approaches.