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We present an approach to teach incrementally human gestures to a humanoid robot. The learning process consists of first projecting the movement data in a latent space and encoding the resulting signals in a Gaussian Mixture Model (GMM). We compare the performance of two incremental training procedures against a batch training procedure. Qualitative and quantitative evaluations are performed on data acquired from motion sensors attached to a human demonstrator and data acquired by kinesthetically demonstrating the task to the robot. We present experiments to show that these different modalities can be used to teach incrementally basketball officials' signals to a HOAP- 3 humanoid robot.