Speech Pseudonymisation Assessment Using Voice Similarity Matrices
Speech Pseudonymisation Assessment Using Voice Similarity Matrices
复制标题
使用语音相似度矩阵进行语音假名化评估
DOI：
10.21437/interspeech.2020-2720
复制
发表时间：
2020
影响因子：
3.6
通讯作者：
N. Evans
中科院分区：
文献类型：
--
作者：
Paul;J. Bonastre;D. Matrouf;N. Tomashenko;A. Nautsch;N. Evans
随着语音技术的发展和隐私保护立法的不断完善，人们迫切需要为语音应用开发隐私保护解决方案。这些都是必不可少的，因为语音信号传达了丰富的，个人的和潜在的敏感信息。匿名化，最近的语音隐私倡议的重点，是一种策略，以保护发言人的身份信息。假名化解决方案的目的不仅是掩盖说话人的身份，并保持语言的内容，质量和自然性，这是匿名化的目标，但也要保持语音的独特性。现有的匿名化评估指标不合适，完全缺乏匿名化评估指标。基于语音相似性矩阵，本文提出了第一个直观的可视化的语音信号和两个新的客观评估指标的语音合成性能。它们反映了两个关键的非身份化要求，即去识别和声音独特性。
The proliferation of speech technologies and rising privacy legislation calls for the development of privacy preservation solutions for speech applications. These are essential since speech signals convey a wealth of rich, personal and potentially sensitive information. Anonymisation, the focus of the recent VoicePrivacy initiative, is one strategy to protect speaker identity information. Pseudonymisation solutions aim not only to mask the speaker identity and preserve the linguistic content, quality and naturalness, as is the goal of anonymisation, but also to preserve voice distinctiveness. Existing metrics for the assessment of anonymisation are ill-suited and those for the assessment of pseudonymisation are completely lacking. Based upon voice similarity matrices, this paper proposes the first intuitive visualisation of pseudonymisation performance for speech signals and two novel metrics for objective assessment. They reflect the two, key pseudonymisation requirements of de-identification and voice distinctiveness.