Detection of cloned voices in realistic forensic voice comparison scenarios

Deepfakes and synthetic audio significantly degrade the performance of automatic speaker recognition systems commonly used in forensic laboratories. We investigate the effectiveness of Mel-Frequency Cepstral Coefficients (MFCCs) for detecting cloned voices, ultimately concluding that MFCC-based methods are insufficient as a universal anti-spoofing tool due to their inability to generalize across different cloning algorithms. Furthermore, we evaluate the performance of the HIVE AI-deepfake Content Detection tool, noting its vulnerability to babble noise and signal saturation, which are common in real-world forensic recordings. This investigation emphasizes the ongoing competition between voice cloning and detection technologies, underscoring the urgent need for more robust and generalized anti-spoofing systems for forensic applications.