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Cycle-consistent generative adversarial networks have been widely used innon-parallel voice conversion (VC). Their ability to learn mappings betweensource and target features without relying on parallel training data eliminatesthe need for temporal alignments. However, most methods decouple the conversionof acoustic features from synthesizing the audio signal by using separatemodels for conversion and waveform synthesis. This work unifies conversion andsynthesis into a single model, thereby eliminating the need for a separatevocoder. By leveraging cycle-consistent training and a self-supervisedauxiliary training task, our model is able to efficiently generate convertedhigh-quality raw audio waveforms. Subjective listening tests show that ourmethod outperforms the baseline in whispered speech conversion (up to 6.7%relative improvement), and mean opinion score predictions yield competitiveresults in conventional VC (between 0.5% and 2.4% relative improvement).

Vocoder-Free Non-Parallel Conversion of Whispered Speech With Masked Cycle-Consistent Generative Adversarial Networks