Self-supervised Optimality-Guided Learning of Speech Articulation

This paper introduces a novel approach for modeling speech articulatory planning based on Optimal Control Theory. The presented approach uses an internal feed-forward controller model that learns to predict optimal articulatory commands minimizing a context-dependent objective function. This objective function combines conflicting tasks of minimizing articulatory effort and maximizing the recognition probability of a target vowel based on acoustic characteristics. We present a self-supervised optimality-guided architecture for training the feedforward internal model that directly uses the objective function as a training loss. Simulations involving isolated vowels of American-English show that online training of the internal model enables feedforward estimation of near-optimal articulatory parameters.