标题： SCDF：用于偏差分析的说话人特征深度伪造语音数据集 显示英文标题

标题： SCDF: A Speaker Characteristics DeepFake Speech Dataset for Bias Analysis

摘要： 尽管对深度伪造语音检测的关注不断增加，但偏差和公平性方面在语音领域仍研究不足。 为解决这一差距，我们引入了说话人特征深度伪造（SCDF）数据集：一个新颖且丰富标注的资源，可系统评估深度伪造语音检测中的人口统计偏差。 SCDF包含超过237,000个发音，在五种语言中平衡代表了男性和女性说话人，并涵盖了广泛的年龄范围。 我们评估了几种最先进的检测器，并表明说话人特征显著影响检测性能，在性别、语言、年龄和合成器类型之间存在差异。 这些发现突显了需要关注偏差的开发，并为构建符合伦理和监管标准的非歧视性深度伪造检测系统提供了基础。 显示英文摘要

摘要： Despite growing attention to deepfake speech detection, the aspects of bias and fairness remain underexplored in the speech domain. To address this gap, we introduce the Speaker Characteristics Deepfake (SCDF) dataset: a novel, richly annotated resource enabling systematic evaluation of demographic biases in deepfake speech detection. SCDF contains over 237,000 utterances in a balanced representation of both male and female speakers spanning five languages and a wide age range. We evaluate several state-of-the-art detectors and show that speaker characteristics significantly influence detection performance, revealing disparities across sex, language, age, and synthesizer type. These findings highlight the need for bias-aware development and provide a foundation for building non-discriminatory deepfake detection systems aligned with ethical and regulatory standards.