Voice conversion (VC) is a technique of exclusively converting speaker-specific information in the source speech while preserving the associated phonemic information. Non-negative matrix factorization (NMF)-based VC has been widely researched because of the natural-sounding voice it achieves when compared with conventional Gaussian mixture model-based VC. In conventional NMF-VC, models are trained using parallel data which results in the speech data requiring elaborate pre-processing to generate parallel data. NMF-VC also tends to be an extensive model as this method has several parallel exemplars for the dictionary matrix, leading to a high computational cost. In this study, an innovative parallel dictionary-learning method using non-negative Tucker decomposition (NTD) is proposed. The proposed method uses tensor decomposition and decomposes an input observation into a set of mode matrices and one core tensor. The proposed NTD-based dictionary-learning method estimates the dictionary matrix for NMF-VC without using parallel data. The experimental results show that the proposed method outperforms other methods in both parallel and non-parallel settings.

中文翻译：

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使用非负 Tucker 分解进行语音转换的非并行字典学习

语音转换 (VC) 是一种在保留相关音素信息的同时专门转换源语音中特定于说话者的信息的技术。与传统的基于高斯混合模型的 VC 相比，基于非负矩阵分解 (NMF) 的 VC 因其可实现自然的声音而得到广泛研究。在传统的 NMF-VC 中，模型使用并行数据进行训练，这导致语音数据需要精细的预处理才能生成并行数据。NMF-VC 也往往是一个扩展模型，因为该方法具有多个并行的字典矩阵示例，导致计算成本高。在这项研究中，提出了一种使用非负塔克分解 (NTD) 的创新并行字典学习方法。所提出的方法使用张量分解并将输入观察分解为一组模式矩阵和一个核心张量。提出的基于 NTD 的字典学习方法在不使用并行数据的情况下估计 NMF-VC 的字典矩阵。实验结果表明，所提出的方法在并行和非并行设置中均优于其他方法。