Linguistic Features Selection in Fundament Frequency Patterns

Yiqiang Chen, Wen Gao, Tingshao Zhu

Abstract: The prosodic pattern generation and prediction is more important for synthesizing natural sounding speech reproduction of input Chinese text. In this paper, the typical pitch models are clustered from a large actual speech database firstly. Then we propose several methods including rough set method and Bayesian relief network on linguistic features selection, which can be directly used to predict pitch, energy, and duration patterns. A comparison between these two methods is proposed and to overcome each disadvantage, we combined the results of these two methods, and coded the most important features to Bayesian relief network firstly. After learning, some experiment shows the F0 model prediction based on the selected features is the same as original one for most pitches.