Attributing a synthetic utterance to its originating system remains an open challenge: closed-set models fail to reject unseen synthesizers and produce overconfident predictions. To address this, we propose a dual-branch gated fusion framework that pairs XLSR-53 with CORES, a 66-dimensional descriptor that, unlike prior Linear Filter Bank (LFB)-only work, spans cepstral, oscillatory, rhythmic, energy, and spectral dimensions to capture complementary synthesis artifacts. Our analysis shows XLSR-53 remains discriminative in-domain (ID) while CORES generalizes stably under distribution shift (OOD), yet their naive concatenation fails due to SSL representational imbalance. To resolve this, an input-conditioned gate adaptively weights each branch under joint training with cross-entropy, an energy margin loss for ID/OOD separation, and a gate diversity term. On the MLAAD benchmark, our system achieves 97.6\% ID accuracy, 4.9\% EERc, and an 83.5\% relative FPR95 reduction over the Interspeech 2025 baseline.

翻译：将合成语音归因到其生成系统仍是一个开放挑战：闭集模型无法拒绝未见过的合成器，并会产生过度自信的预测。为此，我们提出了一种双分支门控融合框架，将XLSR-53与CORES配对。CORES是一个66维描述符，不同于以往仅依赖线性滤波器组的工作，它跨越倒谱、振荡、节奏、能量和频谱维度，以捕获互补的合成伪影。我们的分析表明，XLSR-53在域内仍具判别性，而CORES在分布偏移下能稳定泛化，但由于SSL表示不平衡，它们的简单拼接会失效。为解决这一问题，输入条件门控通过联合训练（包含交叉熵、用于域内/域外分离的能量边界损失以及门控多样性项）自适应地加权每个分支。在MLAAD基准上，我们的系统达到了97.6%的域内准确率、4.9%的EERc，以及与Interspeech 2025基线相比83.5%的相对FPR95降低。