Comparing Two Smoothing Approaches in Estimating Kinematic Parameters.
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 p...
| Publicado en: | Journal of Speech, Language & Hearing Research Vol. 67; no. 5; pp. 1400 - 1413 |
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| Autores principales: | , |
| Formato: | Artículo |
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American Speech-Language-Hearing Association
May2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=177092377&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 177092377 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: May2024 vid: 67 iid: 5 pid: 42 pub: American Speech-Language-Hearing Association artinfo: ui: 177092377 10.1044/2024_JSLHR-23-00325 ppf: 1400 ppct: 13 formats: fmt: @attributes: type: P size: 1.7MB tig: atl: Comparing Two Smoothing Approaches in Estimating Kinematic Parameters. aug: au: Kuberskia, Stephan R. Gafosa, Adamantios I. affil: Department of Linguistics and Cognitive Sciences, University of Potsdam, Germany. su: Self-evaluation Research funding Kinematics Descriptive statistics Body movement Speech therapy Regression analysis sug: subj: Self-evaluation Research funding Kinematics Descriptive statistics Body movement Speech therapy Regression analysis ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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