论文标题
使用McAdams系数的扬声器匿名
Speaker anonymisation using the McAdams coefficient
论文作者
论文摘要
匿名的目的是操纵语音信号，以降低自动方法对说话者识别的可靠性，同时保留语音的其他方面，例如与清晰度和自然性有关的语音。本文报告了一种匿名方法，与当前的其他方法不同，不需要培训数据，它基于众所周知的信号处理技术，并且既高效又有效。所提出的解决方案使用麦克亚当系数来改变语音信号的光谱包络。使用常见语音私人2020数据库和协议得出的结果表明，随机，优化的转换可以在匿名化方面胜过竞争的解决方案，同时仅引起适度的，即使在半知识的隐私对手的情况下，对清晰度的额外降解也是如此。
Anonymisation has the goal of manipulating speech signals in order to degrade the reliability of automatic approaches to speaker recognition, while preserving other aspects of speech, such as those relating to intelligibility and naturalness. This paper reports an approach to anonymisation that, unlike other current approaches, requires no training data, is based upon well-known signal processing techniques and is both efficient and effective. The proposed solution uses the McAdams coefficient to transform the spectral envelope of speech signals. Results derived using common VoicePrivacy 2020 databases and protocols show that random, optimised transformations can outperform competing solutions in terms of anonymisation while causing only modest, additional degradations to intelligibility, even in the case of a semi-informed privacy adversary.
