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Unsupervised cross-lingual speaker adaptation for HMM-based speech synthesis using two-pass decision tree construction

机译:使用两遍决策树构造的无监督跨语言说话者自适应,用于基于HMM的语音合成

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This paper demonstrates how unsupervised cross-lingual adaptation of HMM-based speech synthesis models may be performed without explicit knowledge of the adaptation data language. A two-pass decision tree construction technique is deployed for this purpose. Using parallel translated datasets, cross-lingual and intralingual adaptation are compared in a controlled manner. Listener evaluations reveal that the proposed method delivers performance approaching that of unsupervised intralingual adaptation.
机译:本文演示了如何在无需显式了解自适应数据语言的情况下执行基于HMM的语音合成模型的无监督跨语言自适应。为此目的,采用了两遍决策树构造技术。使用并行翻译的数据集,以受控的方式比较跨语言和跨语言的适应性。听众的评估表明,所提出的方法所提供的性能接近无监督的舌内适应。

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