  Partially manipulating a sentence can greatly change its meaning. Recent work
shows that countermeasures (CMs) trained on partially spoofed audio can
effectively detect such spoofing. However, the current understanding of the
decision-making process of CMs is limited. We utilize Grad-CAM and introduce a
quantitative analysis metric to interpret CMs' decisions. We find that CMs
prioritize the artifacts of transition regions created when concatenating bona
fide and spoofed audio. This focus differs from that of CMs trained on fully
spoofed audio, which concentrate on the pattern differences between bona fide
and spoofed parts. Our further investigation explains the varying nature of
CMs' focus while making correct or incorrect predictions. These insights
provide a basis for the design of CM models and the creation of datasets.
Moreover, this work lays a foundation of interpretability in the field of
partial spoofed audio detection that has not been well explored previously.
