Optimal speech motor control and token-to-token variability: a Bayesian modeling approach

Abstract

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 selects one of the possible strategies, a common approach, in line with optimal motor control theories, 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 proposes an alternative approach by formulating feedforward optimal control in a probabilistic Bayesian modeling framework. This is illustrated by controlling a biomechanical model of the vocal tract for speech production and by comparing it with an existing optimal control model (GEPPETO). The essential elements of this optimal control model are presented first. From them the Bayesian model is constructed in a progressive way. Performance of the Bayesian model is evaluated based on computer simulations and compared to the optimal control model. This approach is shown to be appropriate for solving the speech planning problem while accounting for variability in a principled way.

Robust Sensorimotor Learning during Variable Sentence-Level Speech

“…The results of such experiments have led to the development of prominent models of speech production. This work proposes that the control of speech relies on forward models to predict sensory outcomes of movements, and errors in these predictions drive sensorimotor learning [5][6][7]. However, sensorimotor learning in speech has only been observed following intensive training on a handful of discrete words or perceptually similar sentences.…”

Coordination patterns in Essential Tremor patients with Deep Brain Stimulation: Syllables with low and high complexity

“…Since gestures are goal-directed movements, each gesture has a target. Note that there are multiple ways for achieving a desired motor goal, also in terms of multiple parameter modifications (Patri, Diard, & Perrier, 2015;Cho, 2006). This is an important aspect, especially when studying compensatory articulation in pathological speech.…”

Current Models of Speech Motor Control: A Control-Theoretic Overview of Architectures & Properties

“…The GEPETTO model (Perrier et al, 2005;Patri et al, 2015) is a feedforward model of speech control. Its architecture is shown in Figure 10.…”

“…The model has also been used to replicate patterns of consonant-vowel coarticulation (Perrier et al, 2005). The most recent version of this model (Patri et al, 2015) uses a probabilistic Bayesian model in place of the fixed optimal cost function, and is able to model the trial-to-trial variability seen in real speech. This is unique among current models, as other models generate consistent articulatory trajectories for multiple productions of any given input.…”