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Automatic detection of unnatural word-level segments in unit-selection speech synthesis

机译:在单元选择语音合成中自动检测不自然的词级句段

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摘要

We investigate the problem of automatically detecting unnatural word-level segments in unit selection speech synthesis. We use a large set of features, namely, target and join costs, language models, prosodic cues, energy and spectrum, and Delta Term Frequency Inverse Document Frequency (TF-IDF), and we report comparative results between different feature types and their combinations. We also compare three modeling methods based on Support Vector Machines (SVMs), Random Forests, and Conditional Random Fields (CRFs). We then discuss our results and present a comprehensive error analysis.
机译:我们研究在单元选择语音合成中自动检测不自然的单词级段的问题。我们使用大量功能,即目标和加入成本,语言模型,韵律线索,能量和频谱以及Delta术语频率逆文档频率(TF-IDF),并且我们报告不同特征类型及其组合之间的比较结果。我们还比较了基于支持向量机(SVM),随机森林和条件随机场(CRF)的三种建模方法。然后,我们讨论我们的结果并提出全面的错误分析。

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