警惕过度依赖神经编码检测，合成语音识别需多维度突破
Neural Encoding Detection is Not All You Need for Synthetic Speech Detection
- 提出综合评估当前合成语音检测方法的局限性
- 指出仅靠神经编码检测难以应对未来挑战
- 适合关注语音安全与检测可持续性的研究者
本文综述了合成语音检测领域的现状与新兴趋势，梳理了主流数据驱动方法，讨论了未来研究若仅聚焦于神经编码检测的优劣，并提出了有前景的研究方向建议。与提出新方法或数据集的工作不同，本文旨在引导该领域未来前沿研究，强调过度依赖可能随技术演进而失效的方法所带来的风险。
原文摘要 · Abstract (English)
This paper reviews the current state and emerging trends in synthetic speech detection. It outlines the main data-driven approaches, discusses the advantages and drawbacks of focusing future research solely on neural encoding detection, and offers recommendations for promising research directions. Unlike works that introduce new detection methods or datasets, this paper aims to guide future state-of-the-art research in the field and to highlight the risk of overcommitting to approaches that may not stand the test of time.
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