Abstract
In this research, we propose several effective methods for initial/final (I/F) duration prediction and energy modeling for corpus-based Mandarin singing voice synthesis (SVS). Our goal is to improve the clarity and naturalness of the synthesized singing voices. Firstly, the framework of the I/F duration prediction model is presented. We construct an individual I/F duration prediction model for each category of consonants. Both linguistic/phonetic attributes and music-score information are used as the input features. The support vector machine (SVM) is employed to train each I/F duration prediction model. Secondly, three methods for energy modeling are proposed. In the first method, we use an identical volume to specify the energy of each syllable. In the second method, we adopt the same features used in the I/F duration prediction to predict energy. In the third method, a rule-based approach is designed to modify the energy according to different combinations of pitch and duration. Finally, several experiments and listening tests are conducted to demonstrate the feasibility of the proposed methods. The experimental results indicate that our methods are able to improve both the clarity and naturalness of the synthesized singing voices.