RAF: Relativistic Adversarial Feedback For Universal Speech Synthesis
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel training framework, RAF, that enhances GAN vocoders' performance in speech synthesis by leveraging self-supervised learning and relativistic pairing. This work significantly contributes to the field by addressing the limitations of existing training objectives and demonstrating broad applicability across various datasets and vocoder architectures.
Methodology
The paper introduces a novel training framework called Relativistic Adversarial Feedback (RAF) for GAN-based vocoders, which significantly enhances both in-domain fidelity and generalization to unseen scenarios. The methodology effectively integrates self-supervised learning models to assist discriminators in evaluating sample quality, promoting richer representations in the generator. The use of relativistic pairing for real and fake waveforms is a key innovation that allows for improved modeling of the training data distribution. The framework is well-structured, with clear definitions of the quality gap and discriminator gap, and the adversarial training objective is robustly formulated.
Results & evaluation
The experiments are comprehensive, utilizing multiple datasets to validate the effectiveness of RAF across various GAN-based vocoders. The results demonstrate consistent performance improvements in both objective and subjective metrics, with RAF-trained models outperforming traditional LSGAN models in perceptual quality while using fewer parameters. The inclusion of ablation studies strengthens the evaluation, providing insights into the contributions of different components of the RAF framework.
Reproducibility
The authors provide a link to the source code for reproducing results, which is a positive aspect for ensuring reproducibility. However, the paper could benefit from more detailed descriptions of the experimental setup, including hyperparameter choices and training configurations, to facilitate easier replication by other researchers.
Limitations
The paper acknowledges limitations regarding the computational costs associated with training RAF due to the use of long segments and heavy SSL models. Additionally, the authors do not explore lightweight alternatives or provide rigorous theoretical explanations for the convergence of RAF. There are also ethical considerations regarding the potential misuse of realistic audio deepfakes generated by the framework.
Broader impact
The proposed RAF framework has significant implications for the field of speech synthesis and neural vocoding, particularly in enhancing the quality and generalization capabilities of GAN-based models. The integration of self-supervised learning models opens avenues for further research in resource-efficient settings and could contribute to advancements in applications such as text-to-speech and voice conversion systems. The paper presents a novel training framework, RAF, that enhances GAN vocoders' performance in speech synthesis by leveraging self-supervised learning and relativistic pairing. This work significantly contributes to the field by addressing the limitations of existing training objectives and demonstrating broad applicability across various datasets and vocoder architectures.
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