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Unsupervised intralingual and 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基于HMM的语音合成的无监督语内和跨语说话者适应

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摘要

Hidden Markov model (HMM)-based speech synthesis systems possess several advantages over concatenative synthesis systems. One such advantage is the relative ease with which HMM-based systems are adapted to speakers not present in the training dataset. Speaker adaptation methods used in the field of HMM-based automatic speech recognition (ASR) are adopted for this task. In the case of unsupervised speaker adaptation, previous work has used a supplementary set of acoustic models to estimate the transcription of the adaptation data. This paper first presents an approach to the unsupervised speaker adaptation task for HMM-based speech synthesis models which avoids the need for such supplementary acoustic models. This is achieved by defining a mapping between HMM-based synthesis models and ASR-style models, via a two-pass decision tree construction process. Second, it is shown that this mapping also enables unsupervised adaptation of HMM-based speech synthesis models without the need to perform linguistic analysis of the estimated transcription of the adaptation data. Third, this paper demonstrates how this technique lends itself to the task of unsupervised cross-lingual adaptation of HMM-based speech synthesis models, and explains the advantages of such an approach. Finally, listener evaluations reveal that the proposed unsupervised adaptation methods deliver performance approaching that of supervised adaptation.
机译：基于隐马尔可夫模型（HMM）的语音合成系统比串联合成系统具有多个优势。这样的优势之一是基于HMM的系统适合于训练数据集中不存在的说话者的相对简便性。为此，采用了基于HMM的自动语音识别（ASR）领域中使用的说话人自适应方法。在无人监督的说话人适应的情况下，先前的工作使用了一组声学模型来估计适应数据的转录。本文首先提出了一种针对基于HMM的语音合成模型的无监督说话人自适应任务的方法，该方法避免了对此类补充声学模型的需求。这是通过两次遍历的决策树构造过程定义基于HMM的综合模型与ASR样式模型之间的映射来实现的。其次，表明该映射还可以实现基于HMM的语音合成模型的无监督自适应，而无需对自适应数据的估计转录进行语言分析。第三，本文演示了该技术如何使其适合基于HMM的语音合成模型的无监督跨语言适应，并说明了这种方法的优势。最后，听众评估表明，所提出的无监督适应方法所提供的性能接近有监督适应的性能。

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    Gibson Matthew; Byrne William Joseph;

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  • 年度 2010
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  • 原文格式 PDF
  • 正文语种 {"code":"en","name":"English","id":9}
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  • 入库时间 2022-08-20 20:25:34

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