The remarkable capacity of the speech motor system to adapt to various speech conditions is due to an excess of degrees of freedom, which enables producing similar acoustical properties with different sets of control strategies. To explain how the Central Nervous System (CNS) selects one of the possible strategies , a common approach in the literature, in line with optimal control theory, is to model speech motor planning as the solution of an optimality problem based on cost functions. Despite the success of this approach, one of its drawbacks is the intrinsic contradiction between the concept of optimality and the observed experimental intra-speaker token-to-token variability. The present paper adopts an alternative approach that formulates feedforward optimal control in a probabilistic Bayesian modeling framework. The framework is shown to be well adapted to the combination of constraints of different physical origins. The integration of force constraints adapted to the control of articulation clarity is taken as an illustration of this feature. Pertinence of the results are illustrated by controlling a biomechanical model of the vocal tract for speech production.