One-to-Many and Many-to-One Voice Conversion Based on Eigenvoices

Abstract

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 utterance-pairs 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.