Abstract
We propose Relativistic Adversarial Feedback (RAF), a novel training objective for GAN vocoders that improves in-domain fidelity and generalization to unseen scenarios. Although modern GAN vocoders employ advanced architectures, their training objectives often fail to promote generalizable representations. RAF addresses this problem by leveraging speech self-supervised learning models to assist discriminators in evaluating sample quality, encouraging the generator to learn richer representations. Furthermore, we utilize relativistic pairing for real and fake waveforms to improve the modeling of the training data distribution. Experiments across multiple datasets show consistent gains in both objective and subjective metrics on GAN-based vocoders. Importantly, the RAF-trained BigVGAN-base outperforms the LSGAN-trained BigVGAN in perceptual quality using only 12\% of the parameters. Comparative studies further confirm the effectiveness of RAF as a training framework for GAN vocoders.
Abstract (translated)
我们提出了一种名为相对对抗反馈（RAF）的新型训练目标，用于GAN音高编码器（GAN vocoders），以提高其在域内的保真度和对未见场景的泛化能力。尽管现代GAN音高编码器采用了先进的架构，但它们的训练目标往往无法促进具有通用性的表示形式的学习。通过利用语音自监督学习模型来帮助判别器评估样本质量，RAF解决了这个问题，并鼓励生成器学习更加丰富的表示形式。此外，我们还采用相对配对方法对真实和虚假波形进行处理，以改善对训练数据分布的建模。在多个数据集上的实验表明，在基于GAN的音高编码器上，无论是客观还是主观指标都得到了一致性的改进。尤为重要的是，使用RAF训练的BigVGAN-base仅利用12%的参数量就超过了LSGAN训练的BigVGAN在感知质量方面的表现。比较研究进一步证实了RAF作为GAN音高编码器训练框架的有效性。
URL
https://arxiv.org/abs/2603.11678
