The human speech production system can be fundamentally characterized by the kinematic relationships between low-level articulator variables and relatively high-level tasks. Such kinematics can be illuminating about many system aspects from degrees of freedom and redundancy to dynamics and even control. Since these relationships are generally complex and infeasible to express in closed form, recent work has focused on statistical methods for estimating the relevant relationships from data (Saltzman, 2006, in Dynamics of Speech Production and Perception; Lammert, 2010, Proc. INTERSPEECH). Such methods have been applied to synthetic speech data in order to evaluate their effectiveness, but they have not yet been demonstrated on real data. Here, we apply these methods to real speech data acquired from real-time magnetic resonance imaging. We extract articulator variables that are consistent with those chosen by various articulatory models, and we relate them to high-level task variables such as constriction ...