Rule-learning Based Prosodic Structure Prediction
Sheng Zhao · Zhongwen xinxi xuebao · 2002
In this paper,a rule-learning based approach is proposed to predict prosodic structure from unrestricted Chinese text. Firstly, a speech corpus is collected, whose text is automatically segmented and tagged and further labeled with two-level prosodic structure and syntactic phrase boundaries. Secondly, features related to prosodic structure are extracted with the corresponding boundary types to establish an example database. Lastly, rule-learning algorithms are applied on the database to induce prediction rules by machine. Various experiments have been conducted to select the best features. Two typical learning algorithms(C4. 5 and Transformation-based learning)are experimented and compared with other methods. The paper also suggests general evaluation parameters for prosodic structure prediction. The experiments show that the rule-learning approach can achieve a better accuracy rate of 90% than the others. Thus it is justified as an effective way to prosodic structure prediction.