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.
Figures & tables
Figure 1: An overview of our proposed pipeline. Given an input pair, the audios are encoded and then projected by the pretrained audio encoder E and the projector module P . Separately, acoustic features are extracted and exported to textual form as acoustic evidence. These are combined into the input of LLM, which uses reasoning to generate the final prediction.
Figure 2: Dataset Splits and Sample Counts
Figure 3: Prompt Template for generating synthesized CoT.
Multi-Modal Research Center, KETI, South Korea · Digital Analysis Section, National Forensic Service, South Korea · Department of Artificial Intelligence, Korea University, South Korea