Effects of Speaker Adaptive Training on Tensor-based Arbitrary Speaker Conversion

This paper introduces speaker adaptive training techniques to tensor-based arbitrary speaker conversion. In voice conversion studies, realization of conversion from/to an arbitrary speaker’s voice is one of the important objectives. For this purpose, eigenvoice conversion (EVC), which is based on an eigenvoice Gaussian mixture model (EV-GMM), was proposed. Although the EVC can effectively construct the conversion model for arbitrary target speakers using only a few utterances, increase of the utterances used to construct the conversion model does not always improve the conversion performance. This is because the EV-GMM method has an inherent problem in representation of GMM supervectors. We previously proposed tensor-based speaker space as a solution for this problem, and realized more flexible control of speaker characteristics. In this paper, to aim larger improvement of the performance of VC, speaker adaptive training and tensor-based speaker representation are integrated. The proposed method can construct the flexible and precise conversion model, and experimental results of one-to-many voice conversion demonstrate the effectiveness of the proposed approach.