title: Quantifying speech rhythm abnormalities in the dysarthrias.
abstract: OBJECTIVE
  In this study, the authors examined whether rhythm metrics capable of distinguishing languages with high and low temporal stress contrast also can distinguish among control and dysarthric speakers of American English with perceptually distinct rhythm patterns. Methods Acoustic measures of vocalic and consonantal segment durations were obtained for speech samples from 55 speakers across 5 groups (hypokinetic, hyperkinetic, flaccid-spastic, ataxic dysarthrias, and controls). Segment durations were used to calculate standard and new rhythm metrics. Discriminant function analyses (DFAs) were used to determine which sets of predictor variables (rhythm metrics) best discriminated between groups (control vs. dysarthrias; and among the 4 dysarthrias). A cross-validation method was used to test the robustness of each original DFA.
  
  
  RESULTS
  The majority of classification functions were more than 80% successful in classifying speakers into their appropriate group. New metrics that combined successive vocalic and consonantal segments emerged as important predictor variables. DFAs pitting each dysarthria group against the combined others resulted in unique constellations of predictor variables that yielded high levels of classification accuracy.
  
  
  CONCLUSIONS
  This study confirms the ability of rhythm metrics to distinguish control speech from dysarthrias and to discriminate dysarthria subtypes. Rhythm metrics show promise for use as a rational and objective clinical tool.
authors.affiliation: Department of Speech and Hearing Science, Arizona State University, Tempe, AZ, USA. julie.liss@asu.edu
authors.forename: Julie M
authors.initials: JM
authors.lastname: Liss
authors.forename: Laurence
authors.initials: L
authors.lastname: White
authors.forename: Sven L
authors.initials: SL
authors.lastname: Mattys
authors.forename: Kaitlin
authors.initials: K
authors.lastname: Lansford
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Lotto
authors.forename: Stephanie M
authors.initials: SM
authors.lastname: Spitzer
authors.forename: John N
authors.initials: JN
authors.lastname: Caviness
mesh_terms: D000704:Analysis of Variance; D001259:Ataxia / Q000175:diagnosis / Q000503:physiopathology; D004401:Dysarthria / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D007802:Language; D011237:Predictive Value of Tests; D013060:Speech / Q000502:physiology*; D013061:Speech Acoustics; D013062:Speech Articulation Tests; D013997:Time Factors