Voice Conversion Based on Cross-Domain Features Using Variational Auto Encoders
Table 1: Mean Mel-cepstral distortion [dB] of all non-silent frames from the baseline and proposed frameworks.
['Method Baseline', 'Method  [BOLD] VAE SP-SP', 'SF1-TF1 6.35', 'SF1-TM1  [BOLD] 6.28', 'SM1-TF1 6.46', 'SM1-TM1 6.13']
Objective Evaluations We reported mean Mel-cepstral distortion (MCD) values on the testing set to evaluate the proposed and baseline frameworks. CDVAE SP-SP and CDVAE MCC-MCC) while the baseline VAE framework only performed well in the SP domain (cf. VAE SP-SP). The MCD values of VAE MCC-MCC were almost identical to those before conversion, implying that the VAE framework totally failed in the MCC domain. In addition, CDVAE MCC-MCC even outperformed the baseline VAE SP-SP. To our best knowledge, this is the first time that VAE-based VC successfully works on MCCs. This result demonstrates the potential of our proposed framework in various extensively studied low-dimensional perceptual features. The result further confirmed that the additional cross-domain reconstruction loss and latent similarity loss did play a good role in learning the latent representation of speech.