Parsing Hierarchical Prosodic Structure for Mandarin Speech Synthesis

In Mandarin prosody synthesis by means of hierarchical prosodic structure, the naturalness of the output is reliant largely on the parsing of the prosodic structure. We propose a machine learning approach to improve prosodic structure parsing in cases where full syntax parsing is neglected due to considerations concerning practicality. The novel aspect of our approach is the new attribute in the input vector, which is named connective degree and calculated from the occurrence rate of the punctuation marks between Chinese characters by referring to a large text corpus. The results of experiments show that connective degree yield makes a remarkable contribution to parsing of hierarchical Mandarin prosodic structure.