| Sumario: | 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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