Spectral Mapping Using Prior Re-Estimation of i-Vectors and System Fusion for Voice Conversion

Abstract

In this paper, we propose a new voice conversion (VC) method using i-vectors which consider low-dimensional representation of speech utterances. An attempt is made to restrict the i-vector variability in the intermediate computation of total variability ( $\mathbf {T}$ ) matrix by using a novel approach that uses modified-prior distribution of the intermediate i-vectors. This $\mathbf {T}$ -modification improves the speaker individuality conversion. For further improvement of conversion score and to keep a better balance between similarity and quality, band-wise spectrogram fusion between conventional joint density Gaussian mixture model (JDGMM) and i-vector based converted spectrograms is employed. The fused spectrogram retains more spectral details and leverages the complementary merits of each subsystem. Experiments in terms of objective and subjective evaluation are conducted extensively on CMU ARCTIC database. The results show that the proposed technique can produce a better trade-off between similarity and quality score than other state-of-the-art baseline VC methods. Furthermore, it works better than JDGMM in limited VC training data. The proposed VC performs moderately better (both objective and subjective) than mixture of factor analyzer based baseline VC. In addition, the proposed VC provides better quality converted speech as compared to maximum likelihood-GMM VC with dynamic feature constraint.