  With the rapid advancement in synthetic speech generation technologies, great
interest in differentiating spoof speech from the natural speech is emerging in
the research community. The identification of these synthetic signals is a
difficult task not only for the cutting-edge classification models but also for
humans themselves. To prevent potential adverse effects, it becomes crucial to
detect spoof signals. From a forensics perspective, it is also important to
predict the algorithm which generated them to identify the forger. This needs
an understanding of the underlying attributes of spoof signals which serve as a
signature for the synthesizer. This study emphasizes the segments of speech
signals critical in identifying their authenticity by utilizing the Vocal Tract
System(\textit{VTS}) and Voice Source(\textit{VS}) features.
  In this paper, we propose a system that detects spoof signals as well as
identifies the corresponding speech-generating algorithm. We achieve 99.58\% in
algorithm classification accuracy. From experiments, we found that a VS
feature-based system gives more attention to the transition of phonemes, while,
a VTS feature-based system gives more attention to stationary segments of
speech signals. We perform model fusion techniques on the VS-based and
VTS-based systems to exploit the complementary information to develop a robust
classifier. Upon analyzing the confusion plots we found that WaveRNN is poorly
classified depicting more naturalness. On the other hand, we identified that
synthesizer like Waveform Concatenation, and Neural Source Filter is classified
with the highest accuracy. Practical implications of this work can aid
researchers from both forensics (leverage artifacts) and the speech communities
(mitigate artifacts).
