Information Focus Synthesis Based on Question Answer Chain

Jing Wan, Han Ren
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Abstract

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-cus’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.
基于问答链的信息焦点综合
虽然语音合成技术在近十年来取得了长足的进步,但仍有改进的空间。本文提出了一种基于联合信息结构、句法韵律的语音合成方法,对现有的语音合成系统有了明显的改进。信息焦点韵律分布特征是汉语韵律过程的一个重要参数,对听力自然、言语理解和信息获取具有典型意义。针对信息结构、句法和韵律之间复杂的映射关系,提出了一种有效的信息焦点检索方法来增强自然语音合成。我们利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建模对焦点的F0和时间段进行处理,得到它们的特征模块。基于特征模块,可以显著提高语音合成的准确性和自然度。本文的其余部分组织如下。第二节总结了前人提出的信息焦点提取理论,并推导出一种新的信息焦点提取方法。实验结果见第3节。实验结果见第4节。结束语在最后一节提出。
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