InterBias-SV：说话人验证中的复合条件

中文摘要 AI 辅助

说话人验证系统会遇到噪声、信道失真和语音变化的组合。单独评估每个条件并不能确定它们的影响是否叠加。InterBias-SV 围绕一个四项比较来组织这一问题：联合误差、两个边际误差和一个共同参考。其结果工件包含 17 个实验、12 个编码器标签和六个语音语料库中的 4,068 条评分记录，总计 1200 万次试验评估。三个实验系列包含计算加性对比所需的同语料库项。对于分配给说话人训练编码器的标签，其平均对比在等错误率（EER）上分别为 +0.0026、+0.0088 和 +0.0024，且在不同设置间存在较大差异。这些描述性平均值并不能确立与加性等价的结论：试验匹配、检查点身份以及部分条件元数据仍未得到验证。我们还考察了两个解释问题。接近随机的 EER 可能使加性预测难以解释，但随机性能并非硬性的 EER 上限，且与预测的相关性并不能识别饱和机制。当人口统计差距的干净参考接近零时，其比率不稳定；绝对差距提供了更直接的总结。该基准提供了条件定义、分析脚本以及可解释复合条件比较的明确要求，同时将可重新计算的摘要与需要进一步实验验证的主张分开。

英文摘要

Speaker verification systems encounter combinations of noise, channel distortion, and changes in speech. Evaluating each condition separately does not establish whether their effects add. InterBias-SV organises this question around a four-term comparison: joint error, two marginal errors, and a common reference. Its results artefact contains 4,068 scored records across 17 experiments, 12 encoder labels, and six speech corpora, totalling 12 million trial evaluations. Three experiment families contain the same-corpus terms needed to compute additive contrasts. For labels assigned to speaker-trained encoders, their mean contrasts are +0.0026, +0.0088, and +0.0024 in equal error rate (EER), with larger variation across settings. These descriptive averages do not establish equivalence to additivity: trial matching, checkpoint identity, and parts of the condition metadata remain unverified. We also examine two interpretation problems. Near-chance EER can make additive predictions difficult to interpret, but chance performance is not a hard EER ceiling, and correlation with the prediction does not identify a saturation mechanism. Ratios of demographic gaps are unstable when their clean reference is near zero; absolute gaps provide a more direct summary. The benchmark provides condition definitions, analysis scripts, and explicit requirements for interpretable compound-condition comparisons, while separating recomputable summaries from claims that require further experimental validation.

发表机构

• Deakin University（迪肯大学）

机构由 AI 辅助整理，请以论文原文为准。