面向异步语音匿名的任意到任意说话人属性扰动
Any-to-any Speaker Attribute Perturbation for Asynchronous Voice Anonymization
AI总结：
本文提出任意到任意训练策略和说话人对抗语音生成模型，通过批均值损失将话语匿名化为伪说话人，在VoxCeleb上验证了异步语音匿名的有效性并分析了隐私保护局限性。
摘要：
说话人属性扰动通过使用对抗性扰动后的语音作为匿名输出，为异步语音匿名提供了一种可行的方法。为了增强同一原始说话人的匿名话语之间的身份不可关联性，通常采用定向攻击训练策略，将话语匿名化为一个共同的指定说话人。然而，该策略可能侵犯指定说话人的隐私，因为该说话人是真实存在的说话人。为降低这一风险，本文提出了一种任意到任意训练策略。该策略通过定义批均值损失，将训练小批量中来自不同说话人的话语匿名化为一个共同的伪说话人，该伪说话人近似为小批量中的平均说话人。在此基础上，提出了一种说话人对抗语音生成模型，融合了非定向攻击和任意到任意策略的监督信息。说话人属性扰动被生成并融入原始语音中，以产生其匿名化版本。通过在VoxCeleb数据集上进行的实验，验证了所提模型在异步语音匿名中的有效性。此外，还开展了额外实验，以探索说话人对抗语音在语音隐私保护中的潜在局限性。通过这些实验，我们旨在为未来研究提供见解，涉及其对黑盒说话人提取器和自适应攻击的防护效能，以及对域外数据集的泛化能力和稳定性。音频样本和开源代码已发布在https://github.com/VoicePrivacy/any-to-any-speaker-attribute-perturbation。
英文摘要：
Speaker attribute perturbation offers a feasible approach to asynchronous voice anonymization by employing adversarially perturbed speech as anonymized output. In order to enhance the identity unlinkability among anonymized utterances from the same original speaker, the targeted attack training strategy is usually applied to anonymize the utterances to a common designated speaker. However, this strategy may violate the privacy of the designated speaker who is an actual speaker. To mitigate this risk, this paper proposes an any-to-any training strategy. It is accomplished by defining a batch mean loss to anonymize the utterances from various speakers within a training mini-batch to a common pseudo-speaker, which is approximated as the average speaker in the mini-batch. Based on this, a speaker-adversarial speech generation model is proposed, incorporating the supervision from both the untargeted attack and the any-to-any strategies. The speaker attribute perturbations are generated and incorporated into the original speech to produce its anonymized version. The effectiveness of the proposed model was justified in asynchronous voice anonymization through experiments conducted on the VoxCeleb datasets. Additional experiments were carried out to explore the potential limitations of speaker-adversarial speech in voice privacy protection. With them, we aim to provide insights for future research on its protective efficacy against black-box speaker extractors \textcolor{black}{and adaptive attacks, as well as} generalization to out-of-domain datasets \textcolor{black}{and stability}. Audio samples and open-source code are published in https://github.com/VoicePrivacy/any-to-any-speaker-attribute-perturbation.
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。
