论文标题
演讲者匿名化发行X-Vector的发声挑战挑战2020
Speaker Anonymization with Distribution-Preserving X-Vector Generation for the VoicePrivacy Challenge 2020
论文作者
论文摘要
在本文中，我们提出了一种具有分发性的语音匿名技术，作为我们对2020年语音策略挑战的提交。我们观察到，挑战基线系统会产生彼此非常相似的假X向量，这比从有机扬声器中提取的X量更大。这种差异是由于在匿名过程中平均从一组扬声器的X向量引起的差异，从而导致信息丢失。我们提出了一种新方法来生成伪造的X-向量，该方法通过保留X-矢量的分布及其相似性来克服这些局限性。在拟合用于采样假X媒介的生成模型之前，我们使用人群数据来了解X矢量空间的属性。我们展示了这种方法如何生成X向量，这些X向量更遵循有机扬声器X-向量的预期相似性分布。我们的方法可以轻松地与其他人集成为系统的匿名组件，并消除在匿名期间分发扬声器池的需求。我们的方法导致在男性中最多增加了$ 19.4 \％$的$ 19.4 \％$，而在女性中，女性在招生和试声语言中与基线解决方案相比是我们生成的声音的多样性的情况。
In this paper, we present a Distribution-Preserving Voice Anonymization technique, as our submission to the VoicePrivacy Challenge 2020. We observe that the challenge baseline system generates fake X-vectors which are very similar to each other, significantly more so than those extracted from organic speakers. This difference arises from averaging many X-vectors from a pool of speakers in the anonymization process, causing a loss of information. We propose a new method to generate fake X-vectors which overcomes these limitations by preserving the distributional properties of X-vectors and their intra-similarity. We use population data to learn the properties of the X-vector space, before fitting a generative model which we use to sample fake X-vectors. We show how this approach generates X-vectors that more closely follow the expected intra-similarity distribution of organic speaker X-vectors. Our method can be easily integrated with others as the anonymization component of the system and removes the need to distribute a pool of speakers to use during the anonymization. Our approach leads to an increase in EER of up to $19.4\%$ in males and $11.1\%$ in females in scenarios where enrollment and trial utterances are anonymized versus the baseline solution, demonstrating the diversity of our generated voices.
