Abstract
Neural codec language models enable high-quality discrete speech synthesis, yet their inference remains vulnerable to token-level artifacts and distributional drift that degrade perceptual realism. Rather than relying on preference optimization or retraining, we propose MSpoof-TTS, a training-free inference framework that improves zero-shot synthesis through multi-resolution spoof guidance. We introduce a Multi-Resolution Token-based Spoof Detection framework that evaluates codec sequences at different temporal granularities to detect locally inconsistent or unnatural patterns. We then integrate the spoof detectors into a hierarchical decoding strategy, progressively pruning low-quality candidates and re-ranking hypotheses. This discriminator-guided generation enhances robustness without modifying model parameters. Experiments validate the effectiveness of our framework for robust and high-quality codec-based speech generation.
Abstract (translated)
神经编码语言模型能够生成高质量的离散语音合成，但其推理过程仍然容易受到破坏感知真实性的令牌级伪迹和分布漂移的影响。不同于依赖偏好优化或重新训练的方法，我们提出了MSpoof-TTS，这是一种无训练推断框架，通过多分辨率伪造引导来改进零样本合成效果。 我们引入了一种基于多分辨率令牌的伪造检测框架，该框架能够在不同的时间粒度上评估编码序列以识别局部不一致或不自然的模式。然后，我们将这些伪造检测器整合到一个分层解码策略中，逐步剔除低质量候选者并重新排列假设。这种由鉴别器引导的生成方法可以在无需修改模型参数的情况下增强鲁棒性。 实验验证了我们框架在基于编码的语音生成中的有效性和其提升合成质量和鲁棒性的能力。
URL
https://arxiv.org/abs/2603.05373
