How thresholding in segmentation affects the regression performance of the linear model.

Abstract

Evaluating any model underlying the control of speech requires segmenting the continuous flow of speech effectors into sequences of movements. A virtually universal practice in this segmentation is to use a velocity-based threshold which identifies a movement onset or offset as the time at which the velocity of the relevant effector breaches some threshold percentage of the maximal velocity. Depending on the threshold choice, more or less of the movement's trajectory is left in for model regression. This paper makes explicit how the choice of this threshold modulates the regression performance of a dynamical model hypothesized to govern speech movements.