Advancements in AI-synthesized human voices have created a growing threat of impersonation and disinformation, making it crucial to develop methods to detect synthetic human voices. This study proposes a new approach to identifying synthetic human voices by detecting artifacts of vocoders in audio signals. Most DeepFake audio synthesis models use a neural vocoder, a neural network that generates waveforms from temporal-frequency representations like mel-spectrograms. By identifying neural vocoder processing in audio, we can determine if a sample is synthesized. To detect synthetic human voices, we introduce a multi-task learning framework for a binary-class RawNet2 model that shares the feature extractor with a vocoder identification module. By treating vocoder identification as a pretext task, we constrain the feature extractor to focus on vocoder artifacts and provide discriminative features for the final binary classifier. Our experiments show that the improved RawNet2 model based on vocoder identification achieves high classification performance on the binary task overall. Codes and data can be found at \url{https://github.com/csun22/Synthetic-Voice-Detection-Vocoder-Artifacts}.

翻译：人工智能合成语音技术的进步带来了日益严重的模仿和虚假信息威胁，因此开发检测合成人类语音的方法变得至关重要。本研究提出一种通过检测音频信号中声码器伪造痕迹来识别合成人类语音的新方法。大多数深度伪造音频合成模型都采用神经声码器（一种基于时频表征如梅尔频谱图生成波形的神经网络）。通过识别音频中是否经过神经声码器处理，即可判定样本是否经过合成。为检测合成人类语音，我们提出一种面向二分类RawNet2模型的多任务学习框架，该框架的特征提取器与声码器识别模块共享参数。通过将声码器识别作为前置任务，我们约束特征提取器聚焦于声码器伪造痕迹，并为最终二分类器提供判别性特征。实验表明，基于声码器识别的改进RawNet2模型在二分类任务上整体实现了高分类性能。代码与数据见\url{https://github.com/csun22/Synthetic-Voice-Detection-Vocoder-Artifacts}。