We present a score normalization method, which is suitable for embedded speaker verification system. Proposed score normalization method does not need physical or actual impostor model but utilizes the client model's mean and covariance vector to imitate an impostor model. Therefore, no additional memory for impostor model is required. Furthermore, most parts of score evaluation process for impostor model are identical with that of client model. As a result, computational burden for virtual cohort model is trivial. when comparing with un-normalized likelihood score based speaker verification system, average error rate reduction of the proposed system is higher than 6.7%.