Exploring mutual information for GMM-based spectral conversion

Abstract

In this paper, we propose a maximum mutual information (MMI) training criterion to refine the parameters of the joint density GMM (JDGMM) set to tackle the over-smoothing issue in voice conversion (VC). Conventionally, the maximum likelihood (ML) criterion is used to train a JDGMM set, which characterizes the joint property of the source and target feature vectors. The MMI training criterion, on the other hand, updates the parameters of the JDGMM set to increase its capability on modeling the dependency between the source and target feature vectors, and thus to make the converted sounds closer to the natural ones. The subjective listening test demonstrates that the quality and individuality of the converted speech by the proposed ML followed by MMI (ML+MMI) training method is better that by the ML training method.