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      The interaction between language usage and acoustic correlates of the Kuy register distinction

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      Laboratory Phonology
      Open Library of the Humanities

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          Abstract

          Contact is often cited as an explanation for the convergence of areal features and has been proposed as an explanation for the emergence of tonal languages in Mainland Southeast Asia. The current production study probes this hypothesis by exploring the relationship between tonal language usage and the acoustic correlates of the register distinction in Kuy, a Katuic language, as spoken in a quadrilingual (Kuy, Thai, Lao, Khmer) Kuy community in Northeast Thailand. The results demonstrate greater persistence of fundamental frequency (f0) differences over the course of the vowel alongside more tonal language experience for male speakers; however, analysis of individual differences finds that H1*−H2*, a correlate of voice quality, is the primary cue for male speakers with greater tonal language experience. For female speakers, a tradeoff is found between f0 and voice quality cues alongside tonal language experience at both the group and individual levels. These findings provide evidence for a model by which contact may serve to enhance existing, non-primary cues in a phonological contrast by shifting cue distributions, thereby increasing the likelihood that these cues will come to be perceived as prominent and phonologized.

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          Fitting Linear Mixed-Effects Models Usinglme4

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            FactoMineR: AnRPackage for Multivariate Analysis

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              Scaling regression inputs by dividing by two standard deviations.

              Interpretation of regression coefficients is sensitive to the scale of the inputs. One method often used to place input variables on a common scale is to divide each numeric variable by its standard deviation. Here we propose dividing each numeric variable by two times its standard deviation, so that the generic comparison is with inputs equal to the mean +/-1 standard deviation. The resulting coefficients are then directly comparable for untransformed binary predictors. We have implemented the procedure as a function in R. We illustrate the method with two simple analyses that are typical of applied modeling: a linear regression of data from the National Election Study and a multilevel logistic regression of data on the prevalence of rodents in New York City apartments. We recommend our rescaling as a default option--an improvement upon the usual approach of including variables in whatever way they are coded in the data file--so that the magnitudes of coefficients can be directly compared as a matter of routine statistical practice. (c) 2007 John Wiley & Sons, Ltd.
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                Author and article information

                Journal
                Laboratory Phonology
                Open Library of the Humanities
                1868-6354
                January 8 2023
                April 18 2023
                : 14
                : 1
                Affiliations
                [1 ]Department of Linguistics, University of California Berkeley
                Article
                10.16995/labphon.6531
                44c5c7e3-7c34-4f3e-941e-6acf56214090
                © 2023

                https://creativecommons.org/licenses/by/4.0

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