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Bridging Dynamical Systems and Optimal Trajectory Approaches to Speech Motor Control with Dynamic Movement Primitives

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Journal Article - Open Access

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MIT Lincoln Laboratory Lexington United States

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Current models of speech motor control rely on either trajectory-based control DIVA,GEPPETO, ACT or a dynamical systems approach based on feedback control Task Dynamics, FACTS. While both approaches have provided insights into the speech motor system, it is difficult to connect these findings across models given the distinct theoretical and computational bases of the two approaches. We propose a new extension of the most widely used dynamical systems approach, Task Dynamics, that incorporates many of the strengths of trajectory-based approaches, providing a way to bridge the theoretical divide between what have been two separate approaches to understanding speech motor control. The Task Dynamics TD model posits that speech gestures are governed by point attractor dynamics consistent with a critically damped harmonic oscillator. Kinematic trajectories associated with such gestures should therefore be consistent with a second-order dynamical system, possibly modified by blending with temporally overlapping gestures or altering oscillator parameters. This account of observed kinematics is powerful and theoretically appealing, but may be insufficient to account for deviations from predicted kinematics i.e., changes produced in response to some external perturbations to the jaw, changes in control during acquisition and development, or effects of wordsyllable frequency. Optimization, such as would be needed to minimize articulatory effort, is also incompatible with the current TD model, though the idea that the speech production systems economizes effort has a long history and, importantly, also plays a critical role in current theories of domain-general human motor control. To address these issues, we use Dynamic Movement Primitives DMPs to expand a dynamical systems framework for speech motor control to allow modification of kinematic trajectories by incorporating a simple, learnable forcing term into existing point attractor dynamics.

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  • Human Factors Engineering and Man Machine Systems

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