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Modeling Verbal Behavior Deficits with the Stimulus Control Ratio Equation, SCoRE

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

Jean-François Patri^1,2, Julien Diard^3,4, Pascal Perrier^5,6

¹GIPSA-Lab, Université Grenoble Alpes, 11 Rue des Mathématiques, Saint-Martin-d'Hères, F-38000, Grenoble, France. Jean-Francois.Patri@gipsa-lab.grenoble-inp.fr.

Biological Cybernetics

|October 27, 2015

Summary

This study introduces a Bayesian framework for speech motor control, offering a new way to understand how the brain plans speech. It explains how this probabilistic model accounts for variability in speech production.

Keywords:

Bayesian modeling Optimal motor control Speech motor control Speech sequence motor planning

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Area of Science:

Speech Motor Control
Computational Neuroscience
Biomechanics

Background:

The speech motor system's adaptability stems from redundant degrees of freedom, allowing varied control strategies for consistent acoustic output.
Optimal motor control theories model speech planning as solving cost-function-based optimality problems.
A key limitation of optimality models is their conflict with observed intra-speaker variability in speech production.

Purpose of the Study:

To propose an alternative approach to speech motor planning using a probabilistic Bayesian framework.
To model feedforward optimal control within this Bayesian framework.
To address the challenge of explaining speech variability within motor control theories.

Main Methods:

Formulation of a feedforward optimal control model within a probabilistic Bayesian framework.
Application to a biomechanical model of the vocal tract for speech production.
Comparison of the Bayesian model's performance against an existing optimal control model (GEPPETO) via computer simulations.

Main Results:

The proposed Bayesian model successfully controls the biomechanical vocal tract model for speech production.
The Bayesian approach demonstrates its ability to account for token-to-token variability in a principled manner.
Performance evaluation through simulations shows the Bayesian model's viability compared to the GEPPETO model.

Conclusions:

The Bayesian optimal control framework provides a robust method for solving speech planning problems.
This approach offers a principled way to integrate and explain variability in speech motor control.
The study validates the Bayesian framework as a suitable alternative to traditional optimality models for understanding speech production.