Abstract
Reliable evaluation of modern zero-shot text-to-speech (TTS) models remains challenging. Subjective tests are costly and hard to reproduce, while objective metrics often saturate, failing to distinguish SOTA systems. To address this, we propose Iterate to Differentiate (I2D), an evaluation framework that recursively synthesizes speech using the model's own outputs as references. Higher-quality models exhibit greater resilience to the distributional shift induced by iterative synthesis, resulting in slower performance degradation. I2D exploits this differential degradation to amplify performance gaps and reveal robustness. By aggregating objective metrics across iterations, I2D improves discriminability and alignment with human judgments, increasing system-level SRCC from 0.118 to 0.464 for UTMOSv2. Experiments on 11 models across Chinese, English, and emotion datasets demonstrate that I2D enables more reliable automated evaluation for zero-shot TTS.
Abstract (translated)
对现代零样本文本转语音（TTS）模型的可靠评估仍具有挑战性。主观测试成本高昂且难以复现，而客观指标常趋于饱和，无法区分最先进系统。为此，我们提出“迭代区分法”（I2D）评估框架，该框架递归使用模型自身输出作为参考来合成语音。更高质量的模型对迭代合成引起的分布偏移表现出更强的鲁棒性，导致性能下降更缓慢。I2D利用这种差异化的性能退化来放大系统间差距并揭示鲁棒性差异。通过聚合迭代间的客观指标，I2D提升了判别能力并与人类判断的一致性，使UTMOSv2的系统级SRCC从0.118提升至0.464。在中文、英文及情感数据集上对11个模型的实验表明，I2D能够实现更可靠的零样本文本转语音自动化评估。
URL
https://arxiv.org/abs/2603.24430
