15th Annual Conference of the International Speech Communication Association

September 14-18, 2014

Motor Control Primitives Arising from a Learned Dynamical Systems Model of Speech Articulation

Vikram Ramanarayanan, Louis Goldstein, Shrikanth S. Narayanan

University of Southern California, USA

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.

Full Paper

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.