Chinese Prosody Structure Prediction Based on Conditional Random Fields
In this paper, a novel statistical method based on Conditional Random Fields (CRF) is proposed for hierarchical prosody structure prediction, which is a key module in speech synthesis systems. We will discuss how to build the prosody models for mandarin Chinese using Conditional Random Fields in detail, including corpus preparation, feature selection, feature template design, model training and evaluation. Comparison is conducted between the new method and the classical decision tree based one. The experimental results show that CRF-based method can significantly improve the overall performance with the same feature set.
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