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Learning HMM from motion capture data for automatic 3D character animation synthesis is becoming a hot spot in research areas of computer graphics and machine learning. To ensure realistic synthesis, the model must be learned to fit the real distribution of human motion. Usually the fitness is measured by likelihood. In this paper, we present a new HMM learning algorithm, which incorporates stochastic optimization technique within the expectation-maximization (EM) learning framework. This algorithm is less prone to be trapped in local optimal and converges faster than traditional Baum-Welch learning algorithm. We apply the new algorithm to learning 3D motion under control of a style variable, which encodes the mood or personality of the performer. Given new style value, motions with corresponding style can be generated from the learned model.