Towards Explicit Acoustic Evidence Perception in Audio LLMs for Speech Deepfake Detection
How much verified proof exists for this claim
One strong evidence source: arxiv
How intriguing or unexplained this claim is
The claim involves active investigation into speech deepfake detection, with multiple competing theories on how to improve detection accuracy by addressing biases in current methods. There are notable unknowns regarding the effectiveness of these approaches and the potential for synthetic speech to evade detection.
Speech deepfake detection (SDD) focuses on identifying whether a given speech signal is genuine or has been synthetically generated. Existing audio large language model (LLM)-based methods excel in content understanding; however, their predictions are often biased toward semantically correlated cues, which results in fine-grained acoustic artifacts being overlooked during the decisionmaking process. Consequently, fake speech with natural semantics can bypass detectors despite harboring subtle...