汉语文语转换系统中停顿指数的自动标注

Assigning Break Indices for Unrestricted Texts in Mandarin Text to Speech System

本文采用了一个基于C TOBI的停顿指数标注的语料库 ,利用有指导的学习方法对自动停顿指数标注方面做了一些有益的探索。本文共实现了三种方法 :基本的马尔科夫模型 ,引入了词长信息的马尔科夫模型 ,引入词长信息的马尔科夫模型结合基于转换的错误驱动的学习方法。然后通过对 30 0 0句的真实文本进行开放测试 ,以基本的马尔科夫模型的结果作为基准 ,实验结果不断改进 ,最终达到了 78 6 %的准确率 ,错误代价降低了 14 5 %

This paper uses a corpus with break indices based on C-TOBI. Applying supervised learning method, some useful attempts are made in the field of automatic break indices intonation. Three approaches, namely, the basic Markov model approach, the Markov model using word length approach, and the Markov model using word length combining transformation-based error-driven learning approach, are presented. After implementing these three approaches, open tests are made on a corpus of 3,000 sentences. The performances are getting better and the last approach produces the highest accuracy, 78.5%, and results in 14.5% decrease in error-cost taking the result of Markov model as baseline.