Deep neural network (DNN) based acoustic model has made great breakthroughs in speech recognition. However, lower source Sino-Tibetan languages such as Tibetan still need further studies, especially when dealing with dialects. Based on a TDNN acoustic model trained according to lattice-free MMI criteria, this paper demonstrates baseline systems for two Tibetan dialects: U-Tsang and Amdo. Transfer learning is also employed to improve our systems. Experiment results show that for low resource Tibetan dialect recognition, transfer learning can consistently outperform the baseline.