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Personalising speech-to-speech translation: Unsupervised cross-lingual speaker adaptation for HMM-based speech synthesis

机译:个性化语音到语音翻译:基于HMM的语音合成的无监督跨语言说话者自适应

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摘要

In this paper we present results of unsupervised cross-lingual speaker adaptation applied to text-to-speech synthesis. The application of our research is the personalisation of speech-to-speech translation in which we employ a HMM statistical framework for both speech recognition and synthesis. This framework provides a logical mechanism to adapt synthesised speech output to the voice of the user by way of speech recognition. In this work we present results of several different unsupervised and cross-lingual adaptation approaches as well as an end-to-end speaker adaptive speech-to-speech translation system. Our experiments show that we can successfully apply speaker adaptation in both unsupervised and cross-lingual scenarios and our proposed algorithms seem to generalise well for several language pairs. We also discuss important future directions including the need for better evaluation metrics.
机译:在本文中,我们介绍了无监督的跨语言说话者自适应应用于文本到语音合成的结果。我们研究的应用是语音到语音翻译的个性化,其中我们采用HMM统计框架进行语音识别和合成。该框架提供了一种逻辑机制,可以通过语音识别将合成的语音输出适配到用户的语音。在这项工作中,我们介绍了几种不同的无监督和跨语言自适应方法以及端到端说话者自适应语音到语音翻译系统的结果。我们的实验表明,我们可以在无人监督和跨语言场景中成功应用说话人自适应,而且我们提出的算法似乎可以很好地推广几种语言对。我们还将讨论重要的未来方向,包括需要更好的评估指标。

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