DIVA: A Self-organizing Neural Network Model for Motor Equivalent Speech Production

Abstract

This paper describes a model of speech production called DIVA that highlights issues of selforganization and motor equivalent production of phonological units. The model uses an actionperception cycle to learn two mappings between three levels of representation. Data on the plasticity of phonemic perceptual boundaries motivates a learned mapping between phoneme representations and vocal tract variables. A second mapping between vocal tract variables and articulator movements is also learned. To achieve the flexible control made possible by the redundancy of this mapping, desired directions in vocal tract configuration space are mapped into articulator velocity commands. Because each vocal tract direction cell learns to activate several articulator velocities during babbling, the model provides a natural account of the formation of coordinative structures. Model simulations show automatic compensation for unexpected constraints despite no previous experience or learning under these constraints.

Supported in part by grants NSF IRI-90-24877 and AFOSR F49620-92-J-0499.