Title: Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks,
Abstract: We propose a parallel-data-free voice-conversion (VC) method that can learn a
mapping from source to target speech without relying on parallel data. The
proposed method is general purpose, high quality, and parallel-data free and
works without any extra data, modules, or alignment procedure. It also avoids
over-smoothing, which occurs in many conventional statistical model-based VC
methods. Our method, called CycleGAN-VC, uses a cycle-consistent adversarial
network (CycleGAN) with gated convolutional neural networks (CNNs) and an
identity-mapping loss. A CycleGAN learns forward and inverse mappings
simultaneously using adversarial and cycle-consistency losses. This makes it
possible to find an optimal pseudo pair from unpaired data. Furthermore, the
adversarial loss contributes to reducing over-smoothing of the converted
feature sequence. We configure a CycleGAN with gated CNNs and train it with an
identity-mapping loss. This allows the mapping function to capture sequential
and hierarchical structures while preserving linguistic information. We
evaluated our method on a parallel-data-free VC task. An objective evaluation
showed that the converted feature sequence was near natural in terms of global
variance and modulation spectra. A subjective evaluation showed that the
quality of the converted speech was comparable to that obtained with a Gaussian
mixture model-based method under advantageous conditions with parallel and
twice the amount of data.