论文标题
识别源扬声器，用于基于语音转换的欺骗攻击对扬声器验证系统的攻击
Identifying Source Speakers for Voice Conversion based Spoofing Attacks on Speaker Verification Systems
论文作者
论文摘要
自动扬声器验证系统旨在验证语音信号的扬声器身份。但是，语音转换系统可以操纵一个人的语音信号，以使其听起来像是另一个说话者的声音并欺骗扬声器验证系统。大多数基于语音转换的欺骗攻击的对策旨在区分说话者验证系统的欺骗语音中的真正的语音。在本文中，我们研究了源说话者识别的问题 - 鉴于语音转换的语音，推断了来源发言人的身份。为了执行源说话者的标识，我们只需在嵌入网络培训的扬声器嵌入式网络培训期间，将源说话者身份标签添加到源说话者身份的标签中。实验结果表明，通过相同语音转换模型的转换语音进行训练和测试时，来源说话者识别的可行性。此外，我们的结果表明，从各种语音转换模型中获得更多转换的话语进行培训有助于改善源说话者的识别性能，从看不见的语音转换模型转换的话语中的识别性能。
An automatic speaker verification system aims to verify the speaker identity of a speech signal. However, a voice conversion system could manipulate a person's speech signal to make it sound like another speaker's voice and deceive the speaker verification system. Most countermeasures for voice conversion-based spoofing attacks are designed to discriminate bona fide speech from spoofed speech for speaker verification systems. In this paper, we investigate the problem of source speaker identification -- inferring the identity of the source speaker given the voice converted speech. To perform source speaker identification, we simply add voice-converted speech data with the label of source speaker identity to the genuine speech dataset during speaker embedding network training. Experimental results show the feasibility of source speaker identification when training and testing with converted speeches from the same voice conversion model(s). In addition, our results demonstrate that having more converted utterances from various voice conversion model for training helps improve the source speaker identification performance on converted utterances from unseen voice conversion models.
