TranssionADD：用于音频深度伪造检测的多帧强化序列标注模型
TranssionADD: A multi-frame reinforcement based sequence tagging model for audio deepfake detection
AI总结：
针对音频深度伪造检测挑战赛（ADD 2023）Track 2的篡改区域定位难题，提出TranssionADD系统，通过适配序列标注任务、数据增强技术及引入多帧检测模块与孤立帧惩罚损失，有效提升了模型的鲁棒性与泛化能力，最终获得第二名。
摘要：
得益于端到端语音建模技术的最新进展，模仿和克隆用户语音变得越来越可行。这导致在区分真实和伪造音频片段方面面临重大挑战。为了解决用户语音滥用和误用的问题，第二届音频深度伪造检测挑战赛（ADD 2023）旨在检测和分析深度伪造语音话语。具体而言，名为Manipulation Region Location (RL)的Track 2旨在精确定位音频中被篡改区域的位置，这些区域可能同时存在于真实和生成的音频片段中。我们提出了新颖的TranssionADD系统，以解决该挑战赛中模型鲁棒性和音频片段异常值这一难题。我们的系统提供了三项独特贡献：1）我们将序列标注任务适配于音频深度伪造检测；2）我们通过各种数据增强技术来提升模型的泛化能力；3）我们引入了多帧检测（MFD）模块，以克服单帧提供的有限表征，并使用孤立帧惩罚（IFP）损失来处理片段中的异常值。我们表现最佳的提交在Track 2中获得了第二名，证明了我们提出的系统的有效性和鲁棒性。
英文摘要：
Thanks to recent advancements in end-to-end speech modeling technology, it has become increasingly feasible to imitate and clone a user`s voice. This leads to a significant challenge in differentiating between authentic and fabricated audio segments. To address the issue of user voice abuse and misuse, the second Audio Deepfake Detection Challenge (ADD 2023) aims to detect and analyze deepfake speech utterances. Specifically, Track 2, named the Manipulation Region Location (RL), aims to pinpoint the location of manipulated regions in audio, which can be present in both real and generated audio segments. We propose our novel TranssionADD system as a solution to the challenging problem of model robustness and audio segment outliers in the trace competition. Our system provides three unique contributions: 1) we adapt sequence tagging task for audio deepfake detection; 2) we improve model generalization by various data augmentation techniques; 3) we incorporate multi-frame detection (MFD) module to overcome limited representation provided by a single frame and use isolated-frame penalty (IFP) loss to handle outliers in segments. Our best submission achieved 2nd place in Track 2, demonstrating the effectiveness and robustness of our proposed system.
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。
