用于最佳文本选择的改进贪婪算法

对于开放域的文语转换系统来说，最佳训练文本集的选择是至关重要的，尤其在希望文语转换２系统能实现对不同说话人语音的自适应建模时，以音长模型为例，在线性模型的假设下，通过对设计矩阵结构的扩展，提出了基于多模型合并的贪婪选择算法。实验表明，由于充分利用了不同子类模型选择文本间的巨大冗余度，从而显著减少了所需训练语句的数量。同时，通过改进拟阵覆盖问题中你价函数的形式，将算法进一步推广至使训练文本中音素总数

Modified Greedy Algorithm for Optimal Text Selection

Optimal text selection is vital for open domain text to speech synthesis, especially when we desire the synthesizer to realize speaker adaptive modeling. The duration model was taken as a test case. Under the linear model assumption, by modifying the structure of design matrix, a multi model based greedy algorithm was proposed. Taking advantage of the big redundancy between texts for different sound categories, it can reduce the number of selected sentences drastically compared to the original single model based version. With a further modification of the cost function in matroid cover problem, the algorithm can be generalized from the minimization of sentence number to the minimization of total phoneme number, so as to implement a more accurate optimal text selection.