Voice conversion (VC), as a voice style transfer technology, is becoming increasingly prevalent while raising serious concerns about its illegal use. Proactively tracing the origins of VC-generated speeches, i.e., speaker traceability, can prevent the misuse of VC, but unfortunately has not been extensively studied. In this paper, we are the first to investigate the speaker traceability for VC and propose a traceable VC framework named VoxTracer. Our VoxTracer is similar to but beyond the paradigm of audio watermarking. We first use unique speaker embedding to represent speaker identity. Then we design a VAE-Glow structure, in which the hiding process imperceptibly integrates the source speaker identity into the VC, and the tracing process accurately recovers the source speaker identity and even the source speech in spite of severe speech quality degradation. To address the speech mismatch between the hiding and tracing processes affected by different distortions, we also adopt an asynchronous training strategy to optimize the VAE-Glow models. The VoxTracer is versatile enough to be applied to arbitrary VC methods and popular audio coding standards. Extensive experiments demonstrate that the VoxTracer achieves not only high imperceptibility in hiding, but also nearly 100% tracing accuracy against various types of audio lossy compressions (AAC, MP3, Opus and SILK) with a broad range of bitrates (16 kbps - 128 kbps) even in a very short time duration (0.74s). Our speech demo is available at https://anonymous.4open.science/w/DEMOofVoxTracer.

翻译：语音转换作为一种声音风格迁移技术，正日益普及，同时也引发了对其非法使用的严重担忧。主动追溯语音转换生成语音的源头（即说话人溯源）可以防止语音转换的滥用，但遗憾的是，该领域尚未得到广泛研究。在本文中，我们首次针对语音转换的说话人溯源问题展开研究，并提出了一种名为VoxTracer的可溯源语音转换框架。我们的VoxTracer类似于音频水印范式，但又超越了该范式。我们首先使用独特的说话人嵌入向量来表示说话人身份。随后，我们设计了一种VAE-Glow结构，其中隐藏过程将源说话人身份以难以察觉的方式集成到语音转换中，而溯源过程即使在语音质量严重下降的情况下，也能准确恢复源说话人身份乃至源语音。为解决不同畸变影响下隐藏与溯源过程之间的语音不匹配问题，我们还采用异步训练策略来优化VAE-Glow模型。VoxTracer具有极强的通用性，可应用于任意语音转换方法和主流音频编码标准。大量实验表明，VoxTracer不仅实现了高隐蔽性的隐藏效果，而且在面对多种类型音频有损压缩（AAC、MP3、Opus和SILK）及宽比特率范围（16 kbps-128 kbps）时，即使时间间隔极短（0.74秒），也能达到近乎100%的溯源准确率。我们的语音演示可在https://anonymous.4open.science/w/DEMOofVoxTracer获取。