  Recent progress in generative AI technology has made audio deepfakes
remarkably more realistic. While current research on anti-spoofing systems
primarily focuses on assessing whether a given audio sample is fake or genuine,
there has been limited attention on discerning the specific techniques to
create the audio deepfakes. Algorithms commonly used in audio deepfake
generation, like text-to-speech (TTS) and voice conversion (VC), undergo
distinct stages including input processing, acoustic modeling, and waveform
generation. In this work, we introduce a system designed to classify various
spoofing attributes, capturing the distinctive features of individual modules
throughout the entire generation pipeline. We evaluate our system on two
datasets: the ASVspoof 2019 Logical Access and the Multi-Language Audio
Anti-Spoofing Dataset (MLAAD). Results from both experiments demonstrate the
robustness of the system to identify the different spoofing attributes of
deepfake generation systems.
