VoiceGrad: Non-Parallel Any-to-Many Voice Conversion With Annealed Langevin Dynamics

Hirokazu Kameoka, Takuhiro Kaneko, Kou Tanaka, Nobukatsu Hojo, Shogo Seki

Abstract: In this paper, we propose a non-parallel any-to-many voice conversion (VC) method termed VoiceGrad. Inspired by WaveGrad, a recently introduced novel waveform generation method, VoiceGrad is based upon the concepts of score matching, Langevin dynamics, and diffusion models. The idea involves training a score approximator, a fully convolutional network with a U-Net structure, to predict the gradient of the log density of the speech feature sequences of multiple speakers. The trained score approximator can be used to perform VC by using annealed Langevin dynamics or reverse diffusion process to iteratively update an input feature sequence towards the nearest stationary point of the target distribution. Thanks to the nature of this concept, VoiceGrad enables any-to-many VC, a VC scenario in which the speaker of input speech can be arbitrary, and allows for non-parallel training, which requires no parallel utterances.