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Representing fundamental frequency contours generated by HMM-based speech synthesis using generation process model

机译:使用生成过程模型表示通过基于HMM的语音合成生成的基本频率轮廓

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Frame-by-frame representation is not appropriate for prosodic features, which are tightly related to speech units spreading a wide time span, such as words, phrases and so on. This causes an inherit problem in fundamental frequency (F0) contour generation by HMM-based speech synthesis. A method is developed to modify F0 contours in the framework of a generation process model by referring to linguistic information of input text (word boundary and accent type). It takes F0 variances obtained through HMM-based speech synthesis into account during the process. Through a listening experiment on synthetic speech, the method is proved to generate better quality as compared to the HMM-based speech synthesis on average. Since the generation process model can clearly relate its commands and linguistic (and para-on- linguistic) information, the method has an additional advantage; changing speech styles, and /or adding further information (such as emphasis) can be easily done through manipulating the commands.
机译:逐帧表示不适用于韵律特征,而韵律特征与传播时间跨度较大的语音单元(如单词,短语等)紧密相关。这会导致通过基于HMM的语音合成在基频(F 0 )轮廓生成中产生继承问题。开发了一种通过参考输入文本的语言信息(词边界和重音类型)在生成过程模型的框架内修改F 0 轮廓的方法。在此过程中,它考虑了通过基于HMM的语音合成获得的F 0 方差。通过对合成语音的聆听实验,与基于HMM的语音合成平均相比,该方法可产生更好的质量。由于生成过程模型可以清楚地将其命令和语言(以及副语言/非语言)信息相关联,因此该方法还有一个优势。更改语音样式和/或添加其他信息(例如重点)可以通过操作命令轻松完成。

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