在现实的法医语音比较场景中检测克隆声音
Pedro Univaso1, Eugenia San Segundo2
1BlackVOX, Buenos Aires, Argentina.
Frontiers in artificial intelligence
|December 1, 2025
概括
梅尔频率头系数 (MFCC) 作为克隆声音的通用防伪工具,失败了. 像HIVE AI这样的当前检测工具很容易受到现实世界的法医噪音的影响,这凸显了对先进的语音防伪系统的需求.
科学领域:
- 法医科学 法医科学 法医科学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 深度伪造和合成音频损害了扬声器识别系统.
- 法医实验室依靠这些系统进行证据分析.
研究的目的:
- 评估克隆语音检测的Mel-Frequency Cepstral Coefficients (MFCCs) 的使用情况.
- 评估HIVE AI-deepfake内容检测工具在法医环境中的有效性.
主要方法:
- 调查了MFCC检测语音克隆的能力.
- 对模拟的法医音频条件进行了HIVE AI工具性能测试.
主要成果:
- 基于MFCC的方法在不同的语音克隆算法中显示不够的概括性.
- 艾滋病毒人工智能工具证明了对声和信号和的脆弱性.
结论:
- 在法医语音识别中,MFCC并不是一种通用的防伪解决方案.
- 现有的AI检测工具面临着现实世界法医音频复杂性的挑战.
- 开发强大的,通用的反伪造系统对于法医应用至关重要.
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