System fusion for high-performance voice conversion

Abstract

Recently, a number of voice conversion methods have been developed. These methods attempt to improve conversion performance by using diverse mapping techniques in various acoustic domains, e.g. high-resolution spectra and low-resolution Mel-cepstral coefficients. Each individual method has its own pros and cons. In this paper, we introduce a system fusion framework, which leverages and synergizes the merits of these state-of-the-art and even potential future conversion methods. For instance, methods delivering high speech quality are fused with methods capturing speaker characteristics, bringing another level of performance gain. To examine the feasibility of the proposed framework, we select two state-of-the-art methods, Gaussian mixture model and frequency warping based systems, as a case study. Experimental results reveal that the fusion system outperforms each individual method in both objective and subjective evaluation, and demonstrate the effectiveness of the proposed fusion framework.

Error Reduction Network for DBLSTM-based Voice Conversion

“…Sparse representation [8][9][10][11] can be seen as a data-driven, non-parametric approach as an alternative to the traditional parametric approaches to voice conversion. Frequency warping based approaches [12][13][14] aim to modify the frequency axis of source spectra towards that of the target. There are also some post-filter approaches for voice conversion to improve the speech quality [15,16].…”