The development of automatic systems that can detect replayed speech has emerged as a significant research challenge for securing voice biometric systems and is the focus of this paper. Specifically, this paper proposes two novel features to capture the static and dynamic characteristics of the signal from the modulation spectrum, which complement short term spectral features for use in replay detection. The modulation spectral centroid frequency feature is proposed as a vector representation of the first order spectral moments of the modulation spectrum. In conjunction to this, the long term spectral average serves to capture the static characteristics of the modulation spectrum. The proposed system, employing a GMM back-end, was evaluated on the ASVSpoof 2017 dataset and found to yield an EER of 6.54%.