The study of the trainable prosodic model for Chinese text to speech system

Mandarin prosody is characterized by its hierarchical structures when it is influenced by the context. An artificial on this, a neural network, with specially weighted factors and optimizing outputs, is described and applied to construct the Mandarin prosodic model in a TTS system for Chinese. Extensive tests show that the structure of the artificial neural network characterizes the Mandarin prosody more exactly than traditional models. Learning rate is speeded up and computational precision is improved, which makes the whole prosodic model more efficient. Furthermore, the paper also stylizes the Mandarin syllable pitch contours with SPiS parameters (Syllable Pitch Stylized Parameters), and analyzes them in adjusting the syllable pitch. It shows that the SPiS parameters effectively characterize the Mandarin syllable pitch contours, and facilitate the establishment of the network model and the prosodic controlling.