We propose a generalized approach to human gesture recognition based on multiple data modalities such as depth video, articulated pose and speech. In our system, each gesture is decomposed into large-scale body motion and local subtle movements such as hand articulation. The idea of learning at multiple scales is also applied to the temporal dimension, such that a gesture is considered as a set of characteristic motion impulses, or dynamic poses. Each modality is first processed separately in short spatio-temporal blocks, where discriminative data-specific features are either manually extracted or learned. Finally, we employ a Recurrent Neural Network for modeling large-scale temporal dependencies, data fusion and ultimately gesture classification. Our experiments on the 2013 Challenge on Multi-modal Gesture Recognition dataset have demonstrated that using multiple modalities at several spatial and temporal scales leads to a significant increase in performance allowing the model to compensate for errors of individual classifiers as well as noise in the separate channels.