考虑针孔效应的语音匿名化中说话者残留减少
Reducing Speaker Residual by Considering Pinhole Effect in Voice Anonymization
AI总结：
本文提出一种带针孔损失的微调策略，用于语音匿名化框架，通过衡量并最小化同一说话者匿名话语的可链接性，减少残留说话者属性，在保持实用性的同时提升隐私保护。
AI中文摘要：
语音匿名化旨在通过抑制说话者身份同时保留语言内容和韵律来保护隐私。然而，非身份表示中的残留说话者属性仍可能增加可链接性并削弱隐私保护。为此，本文提出了一种针对训练良好的语音匿名化框架的微调策略，采用针孔损失以进一步减少残留说话者属性。受针孔效应启发，针孔损失衡量来自同一源说话者的匿名话语的可链接性。通过最小化该损失，可链接性得以降低，从而改善隐私保护。在多个匿名化框架、伪说话者生成方法和数据集上的实验表明，在保持实用性的同时改善了隐私保护。音频样本可在此https URL中找到。
英文摘要：
Voice anonymization aims to protect privacy by suppressing speaker identity while preserving linguistic content and prosody. However, residual speaker attributes in non-identity representations may still increase linkability and weaken privacy protection. To this end, this paper proposes a fine-tuning strategy with a pinhole loss for well-trained voice anonymization frameworks to further reduce residual speaker attributes. Inspired by the pinhole effect, the pinhole loss measures the linkability of anonymized utterances from the same source speaker. By minimizing this loss, linkability is reduced, thereby improving privacy protection. Experiments on multiple anonymization frameworks, pseudo-speaker generation methods, and datasets show improved privacy protection while maintaining utility. Audio samples can be found in https://anonymous.4open.science/r/Pinhole-loss-fine-tunning-4628.
