Recently, GAN-based neural vocoders, such as Parallel WaveGAN and MelGAN have attracted great interest due to their lightweight and parallel structures, enabling them to generate high fidelity waveform in a real-time manner. In this paper, inspired by Relativistic GAN\cite{jolicoeur2018relativistic}, we introduce a novel variant of the LSGAN framework under the context of waveform synthesis, named Pointwise Relativistic LSGAN (PRLSGAN). In this approach, we take the truism score distribution into consideration and combine the original MSE loss with the proposed pointwise relative discrepancy loss to increase the difficulty of the generator to fool the discriminator, leading to improved generation quality. Moreover, PRLSGAN is a general-purposed framework that can be combined with any GAN-based neural vocoder to enhance its generation quality. Experiments have shown a consistent performance boost based on Parallel WaveGAN and MelGAN, demonstrating the effectiveness and strong generalization ability of our proposed PRLSGAN neural vocoders.
翻译:最近,基于GAN的神经蒸气器,如平行波干和MelGAN等,由于它们的轻重和平行结构,吸引了极大的兴趣,使得它们能够实时产生高度忠诚的波形。 在本文中,在相对论GAN\cite{jolicoeur2018相对论的启发下,我们在波形合成的背景下引入了LSGAN框架的新变体,名为Pointwith 相对论LSGAN(PRLSGAN ) 。 在这种方法中,我们考虑到原流体分分布,并将最初的MSE损失与拟议的点对点相对差异损失结合起来,以便增加产生者愚弄歧视者的困难,从而导致一代质量的提高。 此外,PRLSGAN是一个通用框架,可以与任何基于GAN的神经蒸汽器结合起来,以提高其发电质量。 实验表明,在平行波干和MelGAN的基础上,我们拟议的PRSGAN神经蒸气管的效能和强大的普及能力。