CycleGAN-Based High-Quality Non-Parallel Voice Conversion with Spectrogram and WaveRNN
Aoi Kanagaki, Masaya Tanaka, Takashi Nose, Ryohei Shimizu, Akira Ito, Akinori Ito · 2020
This paper proposes Scyclone, a high-quality voice conversion (VC) technique without parallel data training. Scyclone improves speech naturalness and speaker similarity of the converted speech by introducing CycleGAN-based spectrogram conversion with a simplified WaveRNN-based vocoder. In Scyclone, a linear spectrogram is used as the conversion feature, which avoids quality degradation due to extraction errors. The subjective experiments show that Scyclone is significantly better than CycleGAN-VC2, one of the existing state-of-the-art parallel-data-free VC techniques.