用于Deepfake语音检测的生理标记物初步研究
An Initial Study of Physiological Markers for Deepfake Speech Detection
AI总结：
该研究针对传统Deepfake语音检测器在未知场景下性能下降的问题，提出用36个分属呼吸、发声、时间类别的手工生理标记物检测，经实验验证其为Deepfake语音检测提供了有用可解释的信号。
摘要：
传统Deepfake语音检测器主要针对合成伪影，在未见过的攻击或录制条件下性能常下降。然而人类语音包含与呼吸、发声和时间协调相关的生理标记，这些标记源于语音产生系统，合成器可能无法忠实地复现。这促使我们假设可通过识别这些模式的不一致性来检测Deepfake语音，我们使用36个手工制作的生理标记物（分为呼吸、发声和时间三类）对该假设进行测试。我们在合并的英语录音上用逻辑回归和SVM分类器评估标记向量，最佳域内系统达到21.40%的等错误率（EER），而逻辑回归在域外数据集对上达到32.77%。由于这些标记描述特定的语音产生属性，逻辑回归系数可指示较高的标记值是否会推动分类器向真实语音或Deepfake语音倾斜。这些结果提供了初步证据，表明生理标记物为Deepfake语音检测提供了有用且可解释的信号。
英文摘要：
Conventional deepfake speech detectors primarily target synthesis artifacts and often degrade under unseen attacks or recording conditions. Human speech, however, contains physiological markers associated with respiration, phonation, and temporal coordination arising from the speech-production system that synthesizers may not reproduce faithfully. This motivates the hypothesis that deepfake speech can be detected by identifying inconsistencies in these patterns, which we test using 36 handcrafted physiological markers organized into respiratory, vocal, and temporal categories. We evaluate the marker vectors with logistic regression and SVM classifiers on pooled English recordings. The best in-domain system achieves a 21.40% equal error rate (EER), while logistic regression achieves 32.77% on the out-of-domain dataset pair. Since the markers describe specific speech-production properties, the logistic-regression coefficients indicate whether higher marker values push the classifier toward bona fide or deepfake speech. These results provide initial evidence that physiological markers offer a useful, interpretable signal for deepfake speech detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
