The tandem approach is characteristic of Standard metrics can be applied to evaluate the performance of the majority of related work, including studies involving other isolated spoofing detection solutions and others have been proposed biometric traits [10, 11]. to support their evaluation when they are combined with Standard metrics developed for the evaluation of speaker speaker detection. These either have well-known deficiencies or detectors can also be applied to the evaluation of spoof detectors, restrict the architectural approach to combine speaker and spoof also known as countermeasures (CMs); they are both binary detectors. In this paper, we propose an architecture-agnostic classifiers. Alternative metrics proposed in recent years also detection cost function (a-DCF). A generalisation of the original support the evaluation of speaker and spoof detectors when DCF used widely for the assessment of automatic speaker combined [12, 13]. While the combination of speaker and spoof verification (ASV), the a-DCF is designed for the evaluation detectors still constitutes a single, binary classifier with the very of spoofing-robust ASV. Like the DCF, the a-DCF reflects the same original task of accepting bonafide target trials and rejecting cost of decisions in a Bayes risk sense, with explicitly defined anything else, the consideration of spoofing complicates class priors and detection cost model.

It estimates the probability density function of a random variable Fukunaga & Hostetler (1975). The clustering algorithm is applied to a variety of areas, like segmentation images, Tao et al. (2007); Paris & Durand (2007), particularly medical and satellite images Lu et al. (2011); Ai & Xiong (2014); Wu & Luo (2015); Banerjee et al. (2012), videos Wang et al. (2004), and also applied to high dimensional data clustering Saptarshi et al. (2021). An adapted version of mean-shift clustering was applied to short segments speaker clustering Salmun et al. (2016b,a, 2017); Cohen & Lapidot (2021). This algorithm is deterministic and in an iterative procedure estimates the multi-modal probability density function (pdf) via the "climbing" path of each datum to its mode in a multi-modal distribution. All the data points that reached the same mode are grouped to the same cluster.