使用情感嵌入与预训练模型的发言风格依赖扬声器验证
Long Pham Hoang1, Hibiki Takayama1, Masafumi Nishida1
1Department of Computer Science, Faculty of Informatics, Shizuoka University, Hamamatsu Campus, 3-5-1 Johoku, Chuo-ku, Hamamatsu 432-8011, Japan.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
这项研究引入了情感依赖的扬声器验证,增强了在没有额外模型的情况下防止欺骗的安全性. 基于悲伤的认证被证明是最有效的,为传统方法提供了强大的替代方案.
科学领域:
- 生物识别和语音处理
- 人与计算机的交互
- 网络安全 网络安全
背景情况:
- 扬声器验证系统很方便,但很容易受到伪造攻击的攻击.
- 目前的系统通常需要单独的模型来检测伪造的语音.
- 整合说话者和情感特征提供了一种新的方法来增强强性.
研究的目的:
- 开发一种情绪依赖的扬声器验证系统.
- 在没有额外的分类模型的情况下,提高对伪造言论的稳定性.
- 评估情感依赖认证的可用性和有效性.
主要方法:
- 集成扬声器和情感语音特征进行验证.
- 使用预训练模型比较情绪的声学特征.
- 对各种情绪 (愤怒,快乐,悲伤,中立) 进行了用户评估实验.
主要成果:
- 在扬声器验证方面达到1.13%的平均相同错误率,在防伪方面达到17.7%.
- 情绪依赖认证显示,在初始会话后,认知压力减少.
- 基于悲伤的情绪被证明是最有效的,错误率低,用户压力最小.
结论:
- 情感依赖的扬声器验证增强了对伪造的稳定性.
- 悲伤是一种可行的和有效的情绪,用于语音验证.
- 精心设计的情感依赖认证提供了一个实用且安全的解决方案.
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