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MLLR is a parameter transformation technique for both speaker and environment adaptation. When the amount of adaptation data is scarce, it is necessary to do adaptation with regression classes. In this paper, we present a rapid MLLR adaptation algorithm, which is called Multi-layer structure MLLR adaptation with subspace regression classes and tying (SRCMLR). The method groups the Gaussians on a finer acoustic subspace level. The motivation is that clustering at subspaces of lower dimensions results in lower distortion, and there are fewer parameters to be estimated for the subsequent MLLR transformation matrix. On the other hand, the multi-layer structure generates a regression class dynamically for each subspace using the outcome of the former MLLR transformation. By using the transform structure, computation load in performing transformation is much reduced. Experiments in large vocabulary mandarin speech recognition show the advantages of SRCMLLR over the traditional MLLR while the amount adaptation data is scarce.