Chinese Prosody Structure Prediction Based on Conditional Random Fields.
DOI: 10.1109/ICNC.2009.44 Conference: Fifth International Conference on Natural Computation, ICNC 2009, Tianjian, China, 14-16 August 2009, 6 Volumes
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.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.