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Information Focus Synthesis Based on Question Answer Chain

机译:基于问题答案链的信息焦点综合

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While speech synthesis technologies have come a long way in recent ten years, there is still room for improvement. This paper describes a technique called based on joint information structure, syntax and prosody method, which demonstrates noticeable improvements to existing speech synthesis system. As an important parameter for prosody proceedings in mandarin, information focus prosodic distribution features are typical for hearing natural, speech understanding and in-formation acquisition. Because of the complex mapping relation between information structure, syntax and prosody, we present an efficient method for retrieval information focus to augment a naturalness speech synthesis. We use question answering chain to extract information focus and discover them how to move. Then, we adopt feature classification and prosody predictive modeling to deal with fo-cusȁ9;s F0 and time period and obtain them features module. Based on the features module, should significantly increase the accuracy and naturalness of speech synthesis. The rest of this paper is organized as follows. Section 2 summarizes the previously proposed theory for information focus extraction, and derives a new method. Experiments are expressed in Section 3. And experimental results are shown in Section 4. Concluding remarks are presented in the final section.
机译:尽管语音合成技术在最近十年中取得了长足的进步,但仍有改进的空间。本文介绍了一种基于联合信息结构,语法和韵律方法的技术,该技术证明了对现有语音合成系统的显着改进。作为普通话起诉程序的重要参数,信息集中的韵律分布特征是听力自然,语音理解和信息获取的典型特征。由于信息结构,语法和韵律之间复杂的映射关系,我们提出了一种有效的信息检索方法,以增强自然语音的合成。我们使用问答链来提取信息焦点并发现它们如何移动。然后,我们采用特征分类和韵律预测模型来处理焦点9,F0和时间段,并获得它们的特征模块。基于功能模块,应显着提高语音合成的准确性和自然性。本文的其余部分安排如下。第2节总结了先前提出的信息焦点提取理论,并推导了一种新方法。实验在第3节中表示,实验结果在第4节中显示。最后一节中给出结论。

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