基于LSTM自编码器的并行架构,用于深度假音频检测,具有动态剩余编码和功能融合
Priyanka Muruganandham1, Govardhana Rajan Thangasamy1, Sangeetha Jayaraman2
1Department of CSE, Srinivasa Ramanujan Centre, SASTRA Deemed to be University, Kumbakonam, 612 001, India.
Scientific reports
|July 3, 2025
概括
这项研究引入了一个新的深度假冒音频检测模型,LSTM-AE-DRDE,通过分析时间线索和音频特征来提高准确性. 先进的框架有效地区分真实和假的音频,增强对错误信息的安全性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 由于合成语音技术的进步,深度假冒音频检测至关重要.
- 现有的模型面临着诸如时间不一致和弱语境表示等挑战.
研究的目的:
- 为了提高深度假冒音频检测性能.
- 为了解决当前检测模型的局限性.
主要方法:
- 提出了一个新的框架:使用动态残余差异编码 (LSTM-AE-DRDE) 进行长短期内存自动编码器.
- 利用了注意力增强的LSTM与对比学习的时间线索.
- 在音频变体中使用剩余差异来实现真假分离.
- 集成多种语音特征:MFCC,时间,体,波束包和体参数.
主要成果:
- 在五个基准数据集 (90%-97%) 上实现了高分类准确度.
- 与11种最先进的方法相比,其表现优越,ROC-AUC为~98%.
- 根据特征的除研究证实了框架的稳定性.
结论:
- 拟议的LSTM-AE-DRDE框架显著改善了深度假冒音频检测.
- 多种功能和新型编码方法的整合提高了模型的可靠性和性能.
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