Improved decision tree based method forEnglish prosodic phrase boundary prediction
一种改进的基于决策树的英文韵律短语边界预测方法

In English speech synthesis systems, the accuracy of prosodic phrase boundary prediction has a critical influence on the naturalness and intelligibility of synthetic speech. Currently, decision tree based prediction is the most popular method for predicting the prosodic phrase boundaries. However, this method can't build models for specific keywords because of the data balance issue. Besides, it wouldn't be possible to achieve the global optimization by the local optimization search method at prediction stage. Therefore, in order to improve the prediction performance, this paper introduced the conditional probability of prosodic phrases, and used Viterbi algorithm to optimize the prosodic phrase boundary probability and conditional probability simultaneously. Furthermore, it proposed an optimization method for probability distribution of the decision tree nodes, based on location distribution characteristics of keywords in prosodic phrases. The experimental results show that F-Score of phrase boundary prediction increases from 68. 7% to 77. 8% and the non-acceptance rate drops from 22. 4% to 15. 2% after adopting the proposed method.