We present a method to derive a small number of speech motor control primitives that can produce linguistically-interpretable articulatory movements. We envision that such a dictionary of primitives can be useful for speech motor control, particularly in finding a low-dimensional subspace for such control. First, we use the iterative Linear Quadratic Gaussian with Learned Dynamics (iLQG-LD) algorithm to derive (for a set of utterances) a set of stochastically optimal control inputs to a learned dynamical systems model of the vocal tract that produces desired movement sequences. Second, we use a convolutive Nonnegative Matrix Factorization with sparseness constraints (cNMFsc) algorithm to find a small dictionary of control input primitives that can be used to reproduce the aforementioned optimal control inputs that produce the observed articulatory movements. The method performs favorably on both qualitative and quantitative evaluations conducted on synthetic data produced by an articulatory synthesizer. Such a primitives-based framework could help inform theories of speech motor control and coordination.
Bibliographic reference. Ramanarayanan, Vikram / Goldstein, Louis / Narayanan, Shrikanth S. (2014): "Motor control primitives arising from a learned dynamical systems model of speech articulation", In INTERSPEECH-2014, 150-154.