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      A perceptual pathway for voicing-conditioned vowel duration

      Laboratory Phonology: Journal of the Association for Laboratory Phonology
      Ubiquity Press, Ltd.

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          Abstract

          When codas and vowels are cross-spliced, vowels originally produced with voiced codas are perceived as longer than vowels of the same duration produced with voiceless codas. The spliced coda has the opposite effect: Vowels presented with voiced codas are perceived as shorter. To explain what characteristics make vowels produced with voiced codas sound longer than vowels produced with voiceless codas, four experiments tested how acoustic correlates of voicing affect English speakers’ perception of vowel duration. Vowels were manipulated in a ten-step duration continuum, and listeners categorized each vowel as ‘long’ or ‘short.’ Study 1 tested effects of vowel height categories (/æ, ε, ɪ/) and within-category F1. Study 2 tested effects of intensity contour. Study 3 tested effects of spectral tilt. Perceived vowel duration increased with vowel height and rising intensity. Perceived vowel duration decreased with falling intensity and with higher spectral tilt. There was no effect of within-category F1. Study 4 confirmed that the effect of the original coda environment is not specific to English stimuli. The effects of spectral tilt and intensity contour on perceived duration provide a possible perceptual pathway for the development of voicing-conditioned vowel duration; coda voicing influences these characteristics and they in turn influence perceived vowel duration.

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          Fitting Linear Mixed-Effects Models Usinglme4

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              Fitting Linear Mixed-Effects Models Using lme4

              Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
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                Author and article information

                Journal
                Laboratory Phonology: Journal of the Association for Laboratory Phonology
                Ubiquity Press, Ltd.
                1868-6354
                1868-6354
                January 24 2020
                October 28 2020
                : 11
                : 1
                : 18
                Article
                10.5334/labphon.268
                8bbbb40a-5451-4889-892e-a71a50119c61
                © 2020

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

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