  Anti-spoofing for automatic speaker verification is now a well established
area of research, with three competitive challenges having been held in the
last 6 years. A great deal of research effort over this time has been invested
into the development of front-end representations tailored to the spoofing
detection task. One such approach known as constant Q cepstral coefficients
(CQCCs) have been shown to be especially effective in detecting attacks
implemented with a unit selection based speech synthesis algorithm. Despite
their success, they largely fail in detecting other forms of spoofing attack
where more traditional front-end representations give substantially better
results. Similar differences were also observed in the most recent, 2019
edition of the ASVspoof challenge series. This paper reports our attempts to
help explain these observations. The explanation is shown to lie in the level
of attention paid by each front-end to different sub-band components of the
spectrum. Thus far, surprisingly little has been learned about what artefacts
are being detected by spoofing countermeasures. Our work hence aims to shed
light upon signal or spectrum level artefacts that serve to distinguish
different forms of spoofing attack from genuine, bone fide speech. With a
better understanding of these artefacts we will be better positioned to design
more reliable countermeasures.
