This paper describes two flexible frameworks of voice conversion (VC), i.e., one-to-many VC and many-to-one VC. One-to-many VC realizes the conversion from a user's voice as a source to arbitrary target speakers' ones and many-to-one VC realizes the conversion vice versa. We apply eigenvoice conversion (EVC) to both VC frameworks. Using multiple parallel data sets consisting of utterancepairs of the user and multiple pre-stored speakers, an eigenvoice Gaussian mixture model (EV-GMM) is trained in advance. Unsupervised adaptation of the EV-GMM is available to construct the conversion model for arbitrary target speakers in one-to-many VC or arbitrary source speakers in many-to-one VC using only a small amount of their speech data. Results of various experimental evaluations demonstrate the effectiveness of the proposed VC frameworks.

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This paper presents a novel training method of an eigenvoice Gaussian mixture model (EV-GMM) effectively using non-parallel data sets for many-to-many eigenvoice conversion, which is a technique for converting an arbitrary source speaker's voice into an arbitrary target speaker's voice. In the proposed method, an initial EV-GMM is trained with the conventional method using parallel data sets consisting of a single reference speaker and multiple pre-stored speakers. Then, the initial EV-GMM is further refined using non-parallel data sets including a larger number of pre-stored speakers while considering the reference speaker's voices as hidden variables. The experimental results demonstrate that the proposed method yields significant quality improvements in converted speech by enabling us to use data of a larger number of pre-stored speakers.