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Comparing Two Smoothing Approaches in Estimating Kinematic Parameters
Stephan R Kuberski1, Adamantios I Gafos1
1Department of Linguistics and Cognitive Sciences, University of Potsdam, Germany.
Purpose:
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.
Method:
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.
Results:
Substantially smaller regression errors were observed for the spline smoothing than for the filtering approach.
Conclusion:
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.
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