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A fuzzy decision tree-based duration model for Standard Yorùbá text-to-speech synthesis

机译:标准约鲁巴语文本到语音合成的基于模糊决策树的持续时间模型

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In this paper, we present syllable-based duration modelling in the context of a prosody model for Standard Yorùbá (SY) text-to-speech (TTS) synthesis applications. Our prosody model is conceptualised around a modular holistic framework. This framework is implemented using the Relational Tree (R-Tree) techniques. An important feature of our R-Tree framework is its flexibility in that it facilitates the independent implementation of the different dimensions of prosody, i.e. duration, intonation, and intensity, using different techniques and their subsequent integration. We applied the Fuzzy Decision Tree (FDT) technique to model the duration dimension. In order to evaluate the effectiveness of FDT in duration modelling, we have also developed a Classification And Regression Tree (CART) based duration model using the same speech data. Each of these models was integrated into our R-Tree based prosody model.
机译:在本文中,我们在标准Yorùbá(SY)文本到语音(TTS)合成应用的韵律模型的背景下,提出了基于音节的持续时间建模。我们的韵律模型是围绕模块化整体框架概念化的。该框架是使用关系树(R-Tree)技术实现的。我们的R-Tree框架的一个重要特征是它的灵活性,因为它可以使用不同的技术及其后续集成方法来独立实现韵律的不同维度,即时长,语调和强度。我们应用模糊决策树(FDT)技术对持续时间维度进行建模。为了评估FDT在持续时间建模中的有效性,我们还开发了使用相同语音数据的基于分类和回归树(CART)的持续时间模型。这些模型中的每一个都集成到我们基于R-Tree的韵律模型中。

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