Attacking Voice Anonymization Systems with Augmented Feature and Speaker Identity Difference

Autor:	Zhang, Yanzhe, Bi, Zhonghao, Xiao, Feiyang, Yang, Xuefeng, Zhu, Qiaoxi, Guan, Jian
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Sound
Druh dokumentu:	Working Paper
Popis:	This study focuses on the First VoicePrivacy Attacker Challenge within the ICASSP 2025 Signal Processing Grand Challenge, which aims to develop speaker verification systems capable of determining whether two anonymized speech signals are from the same speaker. However, differences between feature distributions of original and anonymized speech complicate this task. To address this challenge, we propose an attacker system that combines Data Augmentation enhanced feature representation and Speaker Identity Difference enhanced classifier to improve verification performance, termed DA-SID. Specifically, data augmentation strategies (i.e., data fusion and SpecAugment) are utilized to mitigate feature distribution gaps, while probabilistic linear discriminant analysis (PLDA) is employed to further enhance speaker identity difference. Our system significantly outperforms the baseline, demonstrating exceptional effectiveness and robustness against various voice anonymization systems, ultimately securing a top-5 ranking in the challenge. Comment: 2 pages, submitted to ICASSP 2025 GC-7: The First VoicePrivacy Attacker Challenge (by invitation)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.19068 Zobrazit plný text záznamu View this record from Arxiv