Modulation spectrum-based post-filter for GMM-based Voice Conversion

Abstract

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.