Abstract
The technique to hide the real identity of speakers is called speaker anonymization. Aiming at deceiving automatic speaker verification (ASV) systems, speaker anonymization is usually conducted by modifying the temporal or spectral properties of original voices, e.g., by pitch scaling, by vocal tract length normalization (VTLN) or by voice conversion (VC). However, the real identity of anonymized speech can be recovered with a careful re-training of ASVs, e.g., data augmentation by anonymizing voices of the same speaker. In order to evaluate the effectiveness of speaker anonymization, a pre-restoration method for both enrollment and testing data is proposed, investigated and compared for the de-anonymization of anonymized voices. Experimental results show that the pre-restoration method is effective to speaker de-anonymization. Moreover, it is also found that the pre-restoration for testing data performs better than that for enrollment data, which would also be useful to other decision-making tasks involving enrollment and testing stages.
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Acknowledgement
The authors would like to thank Linlin Zheng and Yujun Wang for helpful discussions.
Funding
This work was supported by the Natural Science Foundation of Jiangsu Province (BK20180080) and the National Natural Science Foundation of China (62071484).
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Liu, W., Li, J., Wei, C., Sun, M., Zhang, X., Li, Y. (2022). A Novel Method to Evaluate the Privacy Protection in Speaker Anonymization. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2022. Lecture Notes in Computer Science, vol 13339. Springer, Cham. https://doi.org/10.1007/978-3-031-06788-4_51
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