The replay spoofing tries to fool the Automatic Speaker Verification (ASV) system by the recordings of a genuine utterance. Most of the studies have used magnitude-based features and ignored phase-based features for replay detection. However, the phase-based features also affected due to the environmental characteristics during recording. Hence, the phase-based features, such as parameterized Relative Phase Shift (RPS) and Modified Group Delay are used in this paper along with the baseline feature set, namely, Constant Q Cepstral Coefficients (CQCC) and Mel Frequency Cepstral Coefficients (MFCC).We found out that the score-level fusion of magnitude and phase based features are giving better performance than the individual feature set alone on the ASV Spoof 2017 Challenge version 2. In particular, the Equal Error Rate (EER) is 12.58 % on the evaluation set with the fusion of RPS and the CQCC feature sets using Gaussian Mixture Model (GMM) classifier.