  Recent anti-spoofing systems focus on spoofing detection, where the task is
only to determine whether the test audio is fake. However, there are few
studies putting attention to identifying the methods of generating fake speech.
Common spoofing attack algorithms in the logical access (LA) scenario, such as
voice conversion and speech synthesis, can be divided into several stages:
input processing, conversion, waveform generation, etc. In this work, we
propose a system for classifying different spoofing attributes, representing
characteristics of different modules in the whole pipeline. Classifying
attributes for the spoofing attack other than determining the whole spoofing
pipeline can make the system more robust when encountering complex combinations
of different modules at different stages. In addition, our system can also be
used as an auxiliary system for anti-spoofing against unseen spoofing methods.
The experiments are conducted on ASVspoof 2019 LA data set and the proposed
method achieved a 20\% relative improvement against conventional binary spoof
detection methods.
