Comparing Two Smoothing Approaches in Estimating Kinematic Parameters.

Abstract

We compare two signal smoothing and differentiation approaches: a frequently used approach in the speech community of digital filtering with approximation of derivatives by finite differences and a spline smoothing approach widely used in other fields of human movement science. In particular, we compare the values of a classic set of kinematic parameters estimated by the two smoothing approaches and assess, via regressions, how well these reconstructed values conform to known laws about relations between the parameters. Substantially smaller regression errors were observed for the spline smoothing than for the filtering approach. This result is in broad agreement with reports from other fields of movement science and underpins the superiority of splines also in the domain of speech.