An explainability study of the constant Q cepstral coefficient spoofing countermeasure for automatic speaker verification
This work addresses the need for explainability in anti-spoofing countermeasures to design more reliable systems, though it is incremental as it builds on existing methods without introducing new paradigms.
The study tackled the problem of explaining why constant Q cepstral coefficients (CQCCs) are effective for detecting unit selection-based spoofing attacks but fail for other types in automatic speaker verification, finding that the explanation lies in the attention paid to different sub-band spectral components.
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.