An Improvement on Break Tag Prediction for Mandarin Speech

• Main Authors: Chen, Jui-Chuan, 陳睿詮
• Other Authors: Chen, Sin-Horng
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/08866294816508930949

碩士 === 國立交通大學 === 電信工程研究所 === 100 === This thesis proposed an improvement method on break tag prediction for Mandarin speech synthesis. The linguistic features given from parser were utilized for the prediction of break tags due to the lack of prosodic-acoustic features in TTS. Generally, the linguistic features generated by parser belong to the word-level and sentence-level. However, the syntactic and semantic information still remain insufficient even the word-level and sentence-level features are given. In order to improve the break prediction for Mandarin speech, more linguistic features for describing the syntactic and semantic information are needed. This research classifies and labels the common and special word chunk as well as the phrase artificially, analyze the inter-syllable break appeared at special position in word chunk or phrase by using statistic distribution and decision tree, and investigate at last the mutual strength between these special position and the boundary of word chunk and phrase. The analyzed result showed that the inter-syllable break at special position in word chunk are mostly non-break, while the break of special position in word chunk or phrase are affected by the structure of word chunk or phrase. Furthermore, the smaller structure of word chunk and phrase posseses higher probability to follow the rule of mutual strength related between special position and the boundary of word chunk or phrase. The experiment results also showed that the adding of linguistic features of word chunk and phrase can in fact improve the prediction of break tags. Either using the linguistic features to predict inter-syllable break tags statically, or assisting the dynamic search for boundary of prosodic unit could the TTS achieve a more effective capability of break tags prediction. It virtually showed that the addition of word chunk and phrase information is capable of describing the syntactic structure more correctly, and then improve a more precise prediction of break tags.