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2 results for WaveCycleGAN

WaveCycleGAN converts synthetic speech to natural speech using cycle-consistent adversarial networks.

problem Over-smoothing effect in synthetic speech, leading to quality degradation.
method Cycle-consistent adversarial networks for waveform-level modification.
result Improves naturalness of generated speech sounds.

WaveCycleGAN2 improves speech synthesis quality by reducing aliasing.

problem Human ear can still distinguish synthesized speech from natural speech.
method WaveCycleGAN2 uses generators without down/up-sampling modules and combines discriminators from waveform and acoustic parameter domains.
result WaveCycleGAN2 achieves high-quality speech synthesis with comparable mean opinion scores to natural speech.