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

机译:代表基于HMM的语音合成产生的基本频率轮廓使用生成过程模型

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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-/non- 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,其与扩展宽时间跨度的语音单元紧密相关,例如单词,短语等。这导致基于HMM的语音合成的基本频率(F 0 )的继承问题。通过参考输入文本的语言信息(字边界和重音类型),开发了一种方法以在生成过程模型的框架中修改F 0 轮廓。在过程中,通过基于HMM的语音合成获得的F 0 差异。通过对合成语音的聆听试验,证明了与平均肝的语音合成相比,该方法产生了更好的质量。由于生成过程模型可以清楚地涉及其命令和语言(以及副/非语言)信息,因此该方法具有额外的优势;通过操纵命令,可以轻松完成更改语音样式和/或添加更多信息(例如强调)。

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