Data-driven phrasing for speech synthesis in low-resource languages
Data-driven phrasing for speech synthesis in low-resource languages
复制标题
用于低资源语言语音合成的数据驱动短语
DOI：
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发表时间：
2012
期刊：
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通讯作者：
A. Black
中科院分区：
文献类型：
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作者：
Alok Parlikar;A. Black
我们提出了一种方法来建立短语断裂预测模型时，合成低资源语言的文本。这种方法允许建立模型，而不依赖于可用性的词性标注器，或语料库与手注释休息。我们使用用于构建合成语音的相同语音数据来推断声学短语中断。我们在一个小的文本语料库中进行无监督的词性归纳。我们使用这些标签并训练基于语法的短语模型。在本文中，我们展示了语言的结果：英语，葡萄牙语和马拉地语，这表明我们可以使用很少的数据为新语言快速构建非常合理的短语模型。
We present an approach to build phrase break prediction models when synthesizing text in low resource languages. This method allows building models without depending on the availability of part of speech taggers, or corpus with hand annotated breaks. We use the same speech data used for building a synthetic voice, to deduce acoustic phrase breaks. We perform unsupervised part of speech induction over a small text corpus in the language at hand. We use these tags and train a grammar based phrasing model. In this paper, we show results for the languages: English, Portuguese and Marathi, which suggest that we can quickly build very reasonable phrasing models for new languages using very little data.