谁在真正说话？面向语音转换的稳健且通用的说话人溯源
Who is Speaking Actually? Robust and Versatile Speaker Traceability for Voice Conversion
AI总结：
本文首次提出语音转换的说话人溯源问题，并设计VoxTracer框架，通过VAE-Glow结构将源说话人身份不可感知地嵌入并准确恢复，在多种压缩和比特率下实现近100%追踪准确率。
摘要：
语音转换（VC）作为一种语音风格迁移技术，正变得越来越普遍，同时也引发了对其非法使用的严重担忧。主动追溯VC生成语音的来源，即说话人溯源，可以防止VC的滥用，但遗憾的是，这一方向尚未得到广泛研究。在本文中，我们首次研究VC的说话人溯源问题，并提出一个名为VoxTracer的可溯源VC框架。我们的VoxTracer类似于音频水印范式，但又超越了这一范式。我们首先使用独特的说话人嵌入来表示说话人身份。然后，我们设计了一个VAE-Glow结构，其中隐藏过程将源说话人身份以不可感知的方式集成到VC中，而追踪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甚至源语音。为了解决隐藏和追踪过程中因不同失真而导致的语音不匹配问题，我们还采用了一种异步训练策略来优化VAE-Glow模型。VoxTracer具有足够的通用性，可应用于任意VC方法和流行的音频编码标准。大量实验表明，VoxTracer不仅在隐藏方面实现了高不可感知性，而且在各种类型的音频有损压缩（AAC、MP3、Opus和SILK）以及广泛的比特率（16 kbps - 128 kbps）下，即使在非常短的时间（0.74秒）内，也实现了接近100%的追踪准确率。我们的语音演示可在 https://anonymous.4open.science/w/DEMOofVoxTracer 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
