重新审视低频语音信号的隐私性：探讨重采样方法、评估场景和说话人特征
Revisiting the Privacy of Low-Frequency Speech Signals: Exploring Resampling Methods, Evaluation Scenarios, and Speaker Characteristics
AI总结：
本文研究了低频语音信号在隐私保护中的有效性，通过比较抗混叠滤波器对词错误率和语音活动检测的影响，探讨了重采样方法和说话人特征对隐私的影响。
摘要：
尽管现实中的音频记录提供了关于社交动态和会话行为的见解，但它们也引发了对个人敏感数据隐私的担忧。本文探讨了限制记录为低频音频以保护语音内容的有效性。为了将音频信号重采样到不同的采样率，我们比较了采用抗混叠滤波器的效果。隐私增强通过自动语音识别模型的词错误率增加来衡量。对实用性性能的影响则通过语音活动检测模型来测量。我们的实验结果表明，对于干净的记录，训练采样率高达800 Hz的模型能够正确转录大多数单词。对于两种模型，我们分析了说话人性别和音调的影响，并展示了缺少抗混叠滤波器更强烈地损害语音隐私。
英文摘要：
While audio recordings in real life provide insights into social dynamics and conversational behavior, they also raise concerns about the privacy of personal, sensitive data. This article explores the effectiveness of restricting recordings to low-frequency audio to protect spoken content. For resampling the audio signals to different sampling rates, we compare the effect of employing anti-aliasing filtering. Privacy enhancement is measured by an increased word error rate of automatic speech recognition models. The impact on utility performance is measured with voice activity detection models. Our experimental results show that for clean recordings, models trained with a sampling rate of up to 800 Hz transcribe the majority of words correctly. For both models, we analyzed the impact of the speaker's sex and pitch, and we demonstrated that missing anti-aliasing filters more strongly compromise speech privacy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
