The analysis of conversations recorded in everyday life requires privacy protection. In this contribution, we explore a privacy-preserving feature extraction method based on input feature dimension reduction, spectral smoothing and the low-cost speaker anonymization technique based on McAdams coefficient. We assess the utility of the feature extraction methods with a voice activity detection and a speaker diarization system, while privacy protection is determined with a speech recognition and a speaker verification model. We show that the combination of McAdams coefficient and spectral smoothing maintains the utility while improving privacy.

翻译：在日常生活中记录的对话分析需要隐私保护。本研究探索了一种基于输入特征维度缩减、频谱平滑以及基于McAdams系数的低成本说话人匿名技术的隐私保护特征提取方法。我们通过语音活动检测和说话人日记化系统评估特征提取方法的效用，同时利用语音识别和说话人验证模型衡量隐私保护效果。研究结果表明，McAdams系数与频谱平滑的结合在保持效用的同时提升了隐私保护。