The SPLICE method of feature enhancement is known for its powerful performance. It learns a mapping from noisy to clean feature vectors given a set of stereo training data. However, feature vector variation caused by speaker changes conceals noise-induced variation, which is what we want to find in the SPLICE training. In this paper, an improvement of SPLICE by means of speaker-normalization is proposed. The training data is first normalized with respect to speaker variation, and a mapping is learned afterward. CMLLR with a GMM as its target is utilized for the speaker-normalization, where the GMM representing a standard speaker is learned via a novel variant of the speaker adaptive training. The proposed method was evaluated on Aurora2, and achieved a relative word error rate reduction of 38% over the conventional SPLICE.