Many-to-Many Voice Conversion with Out-of-Dataset Speaker Support
Table 2: Granular Comparison of Style Conversion Quality with Different Speaker Genders and/or Datasets
['Source Speakers', 'Target Speakers', 'Accuracy (Percent) Top-1', 'Accuracy (Percent) Top-3', 'Accuracy (Percent) Top-5', 'Accuracy (Percent) Top-10', 'Accuracy (Percent) Top-20']
Out-of-dataset source speakers have only slightly lower accuracy. This is likely because downsampling path of G learns to discard the style of the source speaker while keeping the content; hence out-of-dataset source speakers’ styles do not significantly impact the conversion.