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Synthesis of Child Speech With HMM Adaptation and Voice Conversion

机译：具有HMM自适应和语音转换功能的儿童语音合成

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摘要

The synthesis of child speech presents challenges both in the collection of data and in the building of a synthesizer from that data. We chose to build a statistical parametric synthesizer using the hidden Markov model (HMM)-based system HTS, as this technique has previously been shown to perform well for limited amounts of data, and for data collected under imperfect conditions. Six different configurations of the synthesizer were compared, using both speaker-dependent and speaker-adaptive modeling techniques, and using varying amounts of data. For comparison with HMM adaptation, techniques from voice conversion were used to transform existing synthesizers to the characteristics of the target speaker. Speaker-adaptive voices generally outperformed child speaker-dependent voices in the evaluation. HMM adaptation outperformed voice conversion style techniques when using the full target speaker corpus; with fewer adaptation data, however, no significant listener preference for either HMM adaptation or voice conversion methods was found.
机译：儿童语音的合成在数据收集和从该数据构建合成器中都提出了挑战。我们选择使用基于隐马尔可夫模型（HMM）的系统HTS构建统计参数合成器，因为以前已证明该技术对于有限数量的数据以及在不完美条件下收集的数据表现良好。使用说话者相关和说话者自适应建模技术，并使用不同数量的数据，对合成器的六种不同配置进行了比较。为了与HMM调整进行比较，使用了语音转换技术将现有的合成器转换为目标说话者的特征。说话者自适应语音在评估中通常优于儿童说话者相关语音。使用完整的目标说话者语料库时，HMM自适应性能优于语音转换样式技术；但是，在自适应数据较少的情况下，没有发现针对HMM自适应或语音转换方法的明显的侦听器偏好。

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