The objective of this paper is to extract robust features for
detecting replay spoof attacks on text-independent speaker
verification systems. In the case of replay attacks, prere-
corded utterance of the target speaker is played to the auto-
matic speaker verification system (ASV)to gain unauthorized
access. In such a scenario, the speech signal carries the char-
acteristics of the intermediate recording device as well. In the
proposed approach, the characteristics of the intermediate de-
vice are highlighted by subtracting the contribution of the live
speech in the cepstral domain. An overcomplete dictionary
learned on cepstral features extracted from live speech data,
is used to subtract the contribution of live speech. The resid-
ual captures the characteristics of recording device, and can
be used to distinguish spoof speech signal from live speech
signal. The distribution of the residuals from live and spoof
speech signals are captured using Gaussian mixture models
(GMMs). The likelihood ratio computed from the GMMs
built on spoof and live signals, respectively, is used to de-
tect the spoof attack. The performance of the proposed ap-
proach is evaluated on ASVspoof 2017 evaluation challenge
database. The proposed feature extraction method achieved
11% relative improvement over the base line system built on
the constant-Q cepstral coefficients.