Method for predicting phrase break using static/dynamic feature and Text-to-Speech System and method based on the same

摘要

A break predicting method to which a static feature and a dynamic feature are reflected, a text-to-speech system based on the same, and a method therefor are provided to combine a CART(Classification And Regression Tree) model of the static feature with an HMM(Hidden Markov Model) model of the dynamic feature, generate a new break prediction model, and predict the most corresponding break strength to the meaning of the corresponding sentence through the generated break prediction model. Text data are extracted from a text corpus(S210). Morphological analysis for the extracted text data is performed(S230). A feature parameter is extracted from the morphological analysis result(S240). The voice recording of the extracted text data is performed, and training data are configured(S250). CART modeling is performed on the basis of the training data, and observation probability is calculated(S260). HMM modeling is performed on the basis of the training data, and transition probability is calculated(S270). A break prediction model is generated on the basis of the observation probability and the transition probability(S280). If a sentence is inputted, a break strength for the inputted sentence is predicted through the break prediction model.