Prosody generation using frame-based Gaussian process regression and classification for statistical parametric speech synthesis

Abstract

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 application of GPR to prosodic features, i.e., F0 and phone duration, was not investigated sufficiently because the kernel function was designed for phonetic information only. In this paper, therefore, we propose a kernel function available for multiple units such as syllables, moras, and accent phrases. The proposed kernel function is based on temporal acoustic events like the beginning of accent phrase and the relative position between the target frame and the event is utilized for the kernel function. Experimental results of objective and subjective tests show that the GPR/GPC-based F0 and duration modeling improves the prediction accuracy of acoustic features compared with HMM-based speech synthesis.