Kalveram, K.Th. (1996) A neural oscillator model learning given trajectories, or how an "allo-imitation algorithm" can be implemented into a motor controller. In J. Piek (Hrsg.), Motor control and human skill: A multi-disciplinary perspective. Champaign: Human Kinetics.
Abstract
In speech motor control, the sensorimotor tool transformation is defined transforming the force pattern of the articulatory muscles into speech sounds. This transformation, also called vocal tract transformation, is - similar to the model of the two-jointed arm - partitioned into two parts, namely the transformation relating the muscle forces to the mechano spatial states of the vocal tract (which is analogous to the arms forward dynamics and includes also "natural" interarticulatory couplings), and the transformation relating the mechanospatial states to the speech sounds (wihich is analogous to the arm's forward kinematics). Low level speech motor control then requires to invert both transformations. Assuming reflex-like processing as the principle of control, the inversion of the force to mechano-spatialstate transformation can be performed using the self-imitation algorithm. Due to erraneous learning of this inversion, the controller can fail to decouple the natural inter-articulatory coupling. This causes abnormal feedback loops throough the reflex-like operating neural network, which in turn can cause stuttering if audio-phonatoric coupling is involved in learning.

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