Investigation of prosodie FO layers in hierarchical FO modeling for HMM-based speech synthesis

To address the overall-micro modeling issue of current prosody model in HMM-based speech synthesis, a hierarchical F0 modeling method has been proposed, in which different kinds of pittch patterns are characterized by different prosodie layers and an minimum generation error (MGE) training framework is used to simultaneous optimize F0 models of all layers. This paper investigate the importance of prosodie layers and relationship between prosodie characteristics by this hierarchical F0 modeling method. Cluster number of each layer is modified to balance the accuracy and robustness of each layer, and thus other layers would be influenced due to the additive structure. The importance and relationship are reflected by different systems with different cluster number ratios. The experimental results and conclusion are valuable and helpful to design a hierarchical F0 modeling system.