一种半监督的语音欺骗检测算法,结合了声学统计特征和时频二维特征
Hongliang Fu1,2, Hang Yu1, Xuemei Wang1,2
1Key Laboratory of Food Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China.
Brain sciences
|May 27, 2023
概括
这项研究介绍了一种新的半监督语音欺骗检测算法. 新方法通过结合声学统计和时间频率特征来提高准确性,提高了谎言检测能力.
科学领域:
- 认知神经科学 认知神经科学
- 语音处理 语音处理
- 机器学习 机器学习
背景情况:
- 人类欺骗涉及复杂的认知神经机制.
- 语音欺骗检测模型通常由于不适当的特征选择而遭受糟糕的概括.
- 先进的特征提取对于提高半监督欺骗检测的准确性至关重要.
研究的目的:
- 提出一种新的半监督语音欺骗检测算法.
- 提高欺骗检测模型的概括能力.
- 为了提高检测语言欺骗的准确性.
主要方法:
- 开发了一种混合型半监督神经网络,结合了自动编码网络 (AE) 和平均教师网络.
- 静态的人工统计特征由AE处理,以进行强大的先进特征提取.
- 三维 (3D) 音频谱特征由平均教师网络处理,以获得时间频率信息.
- 在特征融合后应用了一致性规范化,以减轻过度拟合.
主要成果:
- 拟议的算法在自建的语料库上实现了最高的识别准确率68.62%.
- 与基线系统相比,这意味着精度提高了1.2%.
- 该方法有效地提高了概括能力和检测准确度.
结论:
- 混合型半监督方法有效地提取了用于语音欺骗检测的强大功能.
- 结合声学统计和时间频率特征,可以提高模型的概括性和准确性.
- 拟议的算法在半监督语音欺骗检测方面提供了有希望的进步.
相关概念视频
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