Exemplar-based voice conversion in noisy environment
Exemplar-based voice conversion in noisy environment
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DOI：
10.1109/slt.2012.6424242
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发表时间：
2012-12
期刊：
影响因子：
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通讯作者：
R. Takashima;T. Takiguchi;Y. Ariki
中科院分区：
文献类型：
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作者：
R. Takashima;T. Takiguchi;Y. Ariki
本文提出了一种适用于噪声环境的语音转换（VC）技术，其中引入并行示例来编码源语音信号并合成目标语音信号。并行范例（词典）由源范例和目标范例组成，具有由源说话者和目标说话者说出的相同文本。输入源信号被分解为源样本、从输入信号获得的噪声样本及其权重（活动）。然后，通过使用源样本的权重，从目标样本构建转换后的信号。我们使用干净的语音数据和添加噪声的语音数据执行说话人转换任务。通过将其与传统的基于高斯混合模型（GMM）的方法的有效性进行比较，证实了该方法的有效性。
This paper presents a voice conversion (VC) technique for noisy environments, where parallel exemplars are introduced to encode the source speech signal and synthesize the target speech signal. The parallel exemplars (dictionary) consist of the source exemplars and target exemplars, having the same texts uttered by the source and target speakers. The input source signal is decomposed into the source exemplars, noise exemplars obtained from the input signal, and their weights (activities). Then, by using the weights of the source exemplars, the converted signal is constructed from the target exemplars. We carried out speaker conversion tasks using clean speech data and noise-added speech data. The effectiveness of this method was confirmed by comparing its effectiveness with that of a conventional Gaussian Mixture Model (GMM)-based method.