隐私意识的演讲者特征和多式联络特征 工作面试中的关系分析
Candy Olivia Mawalim1, Chee Wee Leong2, Shogo Okada3
1Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 923-1292, Nomi, Japan. candylim@jaist.ac.jp.
Scientific reports
|February 10, 2026
概括
语音匿名化保护了发言人的身份,同时保留了用于情绪检测等应用程序的语音实用性. 信号处理方法提供了良好的隐私和性能平衡,对于敏感的语音数据应用程序至关重要.
科学领域:
- 语音处理 语音处理
- 生物识别信息 生物识别信息
- 保护隐私的机器学习保护隐私
背景情况:
- 越来越多地使用语音数据用于情绪检测和健康分析,增加了隐私风险.
- 语音录音可以揭示敏感的扬声器特征,需要强大的匿名化技术.
- 维护下游任务的语音实用性与身份保护一起至关重要.
研究的目的:
- 调查语音匿名化方法,以保护说话者的身份.
- 评估匿名化对特征推断的语音特征的影响,特别是在工作面试中.
- 评估不同匿名化技术的隐私和实用性之间的权衡.
主要方法:
- 采用基于信号处理的方法,包括相声编码器.
- 使用基于神经音频编解码器的方法进行语音匿名化.
- 进行实验来分析声学参数变化和特征推断性能.
主要成果:
- 基于信号处理的匿名化保持了语音适合特征评估,与原始语音相比.
- 阶段声编码器方法提供了适度的隐私收益与可接受的公用事业权衡.
- 神经音频编解码方法改变了关键的体特征,略降低了特征估计性能,但提供了更大的隐私.
结论:
- 信号处理方法在敏感应用程序 (如工作面试) 中提供了可行的语音匿名化方法.
- 精心配置的神经音频编解码器可以增强隐私,同时保持语音识别和质量评估的实用性.
- 语音匿名化技术必须根据特定应用程序的隐私和实用要求仔细选择.
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