High-pitched excitation generation for glottal vocoding in statistical parametric speech synthesis using a deep neural network

Achieving high quality and naturalness in statistical parametric synthesis of female voices remains to be difficult despite recent advances in the study area. Vocoding is one such key element in all statistical speech synthesizers that is known to affect the synthesis quality and naturalness. The present study focuses on a special type of vocoding, glottal vocoders, which aim to parameterize speech based on modelling the real excitation of (voiced) speech, the glottal flow. More specifically, we compare three different glottal vocoders by aiming at improved synthesis naturalness of female voices. Two of the vocoders are previously known, both utilizing an old glottal inverse filtering (GIF) method in estimating the glottal flow. The third on, denoted as Quasi Closed Phase - Deep Neural Net (QCP-DNN), takes advantage of a recently proposed new GIF method that shows improved accuracy in estimating the glottal flow from high-pitched speech. Subjective listening tests conducted on an US English female voice show that the proposed QCP-DNN method gives significant improvement in synthetic naturalness compared to the two previously developed glottal vocoders.