标题： 使用逐段波形反转在环境声音录音中强制语音内容隐私 显示英文标题

标题： Enforcing Speech Content Privacy in Environmental Sound Recordings using Segment-wise Waveform Reversal

摘要： 环境声音录音通常包含可理解的语音，这引发了隐私问题，限制了数据的分析、共享和重用。 在本文中，我们介绍了一种方法，使语音不可理解，同时保持声学场景的完整性以及整体音频质量。 我们的方法涉及反转波形段以扭曲语音内容。 通过语音活动检测和语音分离流程来增强这一过程，从而更精确地定位语音。 为了展示所提出方法的有效性，我们考虑了一个三部分的评估协议，用于评估：1）使用词错误率（WER）的语音可理解性，2）使用来自广泛使用的预训练模型的声源分类准确率下降（SCAD）的声源可检测性，以及3）使用Fréchet音频距离（FAD）的音频质量，该距离是通过包含未修改语音的参考数据集计算的。 在由语音和环境声音场景的线性混合组成的模拟评估数据集上的实验表明，我们的方法实现了令人满意的语音可理解性降低（97.9% WER），声源可检测性的最小退化（2.7% SCAD），以及高感知质量（FAD为1.40）。 消融研究进一步突出了流程中每个组件的贡献。 我们还表明，将随机拼接纳入我们的语音内容隐私保护方法可以增强算法对尝试恢复干净语音的鲁棒性，但会略微影响音频质量。 显示英文摘要

摘要： Environmental sound recordings often contain intelligible speech, raising privacy concerns that limit analysis, sharing and reuse of data. In this paper, we introduce a method that renders speech unintelligible while preserving both the integrity of the acoustic scene, and the overall audio quality. Our approach involves reversing waveform segments to distort speech content. This process is enhanced through a voice activity detection and speech separation pipeline, which allows for more precise targeting of speech. In order to demonstrate the effectivness of the proposed approach, we consider a three-part evaluation protocol that assesses: 1) speech intelligibility using Word Error Rate (WER), 2) sound sources detectability using Sound source Classification Accuracy-Drop (SCAD) from a widely used pre-trained model, and 3) audio quality using the Fr\'echet Audio Distance (FAD), computed with our reference dataset that contains unaltered speech. Experiments on this simulated evaluation dataset, which consists of linear mixtures of speech and environmental sound scenes, show that our method achieves satisfactory speech intelligibility reduction (97.9% WER), minimal degradation of the sound sources detectability (2.7% SCAD), and high perceptual quality (FAD of 1.40). An ablation study further highlights the contribution of each component of the pipeline. We also show that incorporating random splicing to our speech content privacy enforcement method can enhance the algorithm's robustness to attempt to recover the clean speech, at a slight cost of audio quality.