Digital technology has made possible unimaginable applications come true. It seems exciting to have a handful of tools for easy editing and manipulation, but it raises alarming concerns that can propagate as speech clones, duplicates, or maybe deep fakes. Validating the authenticity of a speech is one of the primary problems of digital audio forensics. We propose an approach to distinguish human speech from AI synthesized speech exploiting the Bi-spectral and Cepstral analysis. Higher-order statistics have less correlation for human speech in comparison to a synthesized speech. Also, Cepstral analysis revealed a durable power component in human speech that is missing for a synthesized speech. We integrate both these analyses and propose a machine learning model to detect AI synthesized speech.

中文翻译：

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使用倒谱和双谱统计检测 AI 合成语音

数字技术使难以想象的应用成为可能。拥有一些用于轻松编辑和操作的工具似乎令人兴奋，但它引发了令人担忧的问题，这些问题可能会以语音克隆、重复或深度伪造的形式传播。验证语音的真实性是数字音频取证的主要问题之一。我们提出了一种利用双谱和倒谱分析来区分人类语音和 AI 合成语音的方法。与合成语音相比，高阶统计数据与人类语音的相关性较小。此外，倒谱分析揭示了合成语音中缺少的人类语音中的持久功率分量。我们整合了这些分析并提出了一个机器学习模型来检测 AI 合成语音。