Modulation Spectrum-Based Post-Filter for GMM-Based Voice Conversion

This paper addresses an over-smoothing effect in Gaussian Mixture Model (GMM)-based Voice Conversion (VC). The flexible use of the statistical approach is one of the major reason why this approach is widely applied to the speech based systems. However, quality degradation by over-smoothed speech parameter converted is unavoidable problem of statistical modeling. One of common approaches to this over-smoothness in conversion step is to compensate generated features, such as Global Variance (GV), that explicitly express the over-smoothing effect. In statistical Text-To-Speech (TTS) synthesis, we have recently introduced a Modulation Spectrum (MS) which is an extended form of GV, and have proposed MS-based Post Filter (MSPF) in Hidden Markov Model (HMM)-based TTS synthesis. In this paper, we apply the MSPF to GMM-based VC. Because the MS of speech parameters is degraded through GMM-based conversion process, we perform the post-filter due to MS modification of converted parameters. The experimental evaluation yields the quality benefits by the proposed post-filter.