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Non-parallel training for voice conversion using background-based alignment of GMMs and INCA algorithm

机译：使用基于背景的GMM对齐和INCA算法进行语音转换的非并行训练

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摘要

Most of the voice conversion (VC) researches have used parallel training corpora to train the conversion function. However, in practice it is not always possible to gather parallel corpora, so the need for non-parallel training methods arises. As a successful non-parallel method, nearest neighbour search step and a conversion step alignment method (INCA) algorithm has attracted a lot of attention in recent years. In this study, the authors propose a new method of non-parallel VC which is based on the INCA algorithm. The authors' method effectively solves the initialisation problem of INCA algorithm. Their proposed initialisation for INCA is done with alignment of Gaussian mixture models (GMM) using universal background model. Results of objective and subjective experiments determined that the authors' proposed method improves the INCA algorithm. It is observed that this superiority holds for different sizes of training material from 10 to 50 training sentences. In terms of mean opinion score, the authors' method scores 0.25 higher in the case of quality and 0.2 higher in the case of similarity to the target speaker compared with traditional INCA. It seems that the authors' proposed method is a suitable frame alignment method for non-parallel corpora in VC task.
机译：大多数语音转换（VC）研究都使用并行训练语料库来训练转换功能。但是，在实践中，并非总是可以收集并行语料库，因此出现了对非并行训练方法的需求。作为一种成功的非并行方法，最近邻搜索步骤和转换步骤对齐方法（INCA）算法近年来引起了很多关注。在这项研究中，作者提出了一种基于INCA算法的非并行VC新方法。作者的方法有效地解决了INCA算法的初始化问题。他们提出的INCA初始化是通过使用通用背景模型对齐高斯混合模型（GMM）来完成的。客观和主观实验的结果确定了作者提出的方法改进了INCA算法。可以看出，这种优势适用于从10到50个训练句子的不同大小的训练材料。就平均意见得分而言，与传统INCA相比，作者的方法在质量上得分高0.25分，在与目标说话者相似的情况下得分高0.2分。看来作者提出的方法是用于VC任务中非并行语料库的合适帧对齐方法。

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