Multiple Non-Negative Matrix Factorization for Many-to-Many Voice Conversion
Ryo Aihara, Tetsuya Takiguchi, Yasuo Ariki · IEEE/ACM Transactions on Audio Speech and Language Processing · 2016
A novel voice conversion (VC) method for arbitrary speakers is proposed. Non-negative matrix factorization (NMF) has recently been applied to exemplar-based VC. It offers noise robustness and naturalness of the converted voice, compared with widely used Gaussian mixture model-based VC. However, because NMF-based VC requires parallel training data from source and target speakers, the voice of arbitrary speakers cannot be converted in this framework. In this study, we propose the multiple non-negative matrix factorization (Multi-NMF) to allow the implementation of many-to-many, exemplar-based VC. Our experimental results demonstrate that the conversion quality of the proposed method is close to that of conventional one-to-one VC, even though the proposed method requires neither the source speakers' spectra, nor the target speakers' spectra, to be included in the training set.