Abstract
Deepfake speech detection systems are often limited to binary classification tasks and struggle to generate interpretable reasoning or provide context-rich explanations for their decisions. These models primarily extract latent embeddings for authenticity detection but fail to leverage structured acoustic evidence such as prosodic, spectral, and physiological attributes in a meaningful manner. This paper introduces CoLMbo-DF, a Feature-Guided Audio Language Model that addresses these limitations by integrating robust deepfake detection with explicit acoustic chain-of-thought reasoning. By injecting structured textual representations of low-level acoustic features directly into the model prompt, our approach grounds the model's reasoning in interpretable evidence and improves detection accuracy. To support this framework, we introduce a novel dataset of audio pairs paired with chain-of-thought annotations. Experiments show that our method, trained on a lightweight open-source language model, significantly outperforms existing audio language model baselines despite its smaller scale, marking a significant advancement in explainable deepfake speech detection.
Abstract (translated)
深度伪造语音检测系统往往局限于二分类任务，难以生成可解释的推理或提供富含上下文的决策依据。这些模型主要提取潜在嵌入以进行真实性检测，却未能以有意义的方式利用韵律、频谱和生理属性等结构化声学证据。本文提出CoLMbo-DF——一种特征引导式音频语言模型，通过将稳健的深度伪造检测与显式声学思维链推理相结合，解决了上述局限性。该方法将低层声学特征的文本化结构化表示直接注入模型提示词，使模型推理有据可依，并提升了检测准确率。为支持该框架，我们引入了首个包含思维链标注的音频对数据集。实验表明，即使在轻量级开源语言模型上训练，我们的方法仍显著优于现有音频语言模型基线，标志着可解释深度伪造语音检测领域的重大进展。
URL
https://arxiv.org/abs/2603.28021
