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      Prevocalic t-glottaling across word boundaries in Midland American English

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          Abstract

          Rates of t-glottaling across word boundaries in both preconsonantal and prevocalic contexts have recently been claimed to be positively correlated with the frequency of occurrence of a given word in preconsonantal contexts ( Eddington & Channer, 2010). Words typically followed by consonants have been argued to have their final /t/s glottaled more often than words less frequently followed by consonants. This paper includes a number of ‘internal’ and ‘external’ predictors in a mixed-effects logistic regression model and has two goals: (1) to replicate the positive correlation of the frequency of occurrence of a word in preconsonantal contexts (its ‘contextual frequency’) with its rates of t-glottaling in both preconsonantal and prevocalic contexts postulated by Eddington and Channer ( 2010), and (2) to quantify the factors influencing the likelihood of t-glottaling across word boundaries in Midland American English. The effect of contextual frequency has been confirmed. This result is argued to support a hybrid view of phonological storage and processing, one including both abstract and exemplar representations. T-glottaling has also been found to be negatively correlated with bigram frequency and speech rate deviation, while positively correlated with young age in female speakers.

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          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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              Moving beyond Kucera and Francis: a critical evaluation of current word frequency norms and the introduction of a new and improved word frequency measure for American English.

              Word frequency is the most important variable in research on word processing and memory. Yet, the main criterion for selecting word frequency norms has been the availability of the measure, rather than its quality. As a result, much research is still based on the old Kucera and Francis frequency norms. By using the lexical decision times of recently published megastudies, we show how bad this measure is and what must be done to improve it. In particular, we investigated the size of the corpus, the language register on which the corpus is based, and the definition of the frequency measure. We observed that corpus size is of practical importance for small sizes (depending on the frequency of the word), but not for sizes above 16-30 million words. As for the language register, we found that frequencies based on television and film subtitles are better than frequencies based on written sources, certainly for the monosyllabic and bisyllabic words used in psycholinguistic research. Finally, we found that lemma frequencies are not superior to word form frequencies in English and that a measure of contextual diversity is better than a measure based on raw frequency of occurrence. Part of the superiority of the latter is due to the words that are frequently used as names. Assembling a new frequency norm on the basis of these considerations turned out to predict word processing times much better than did the existing norms (including Kucera & Francis and Celex). The new SUBTL frequency norms from the SUBTLEX(US) corpus are freely available for research purposes from http://brm.psychonomic-journals.org/content/supplemental, as well as from the University of Ghent and Lexique Web sites.
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                Author and article information

                Contributors
                Journal
                1868-6354
                Laboratory Phonology: Journal of the Association for Laboratory Phonology
                Ubiquity Press
                1868-6354
                22 September 2020
                2020
                : 11
                : 1
                : 13
                Affiliations
                [1 ]Department of Contemporary English Language, Faculty of English at Adam Mickiewicz University, Poznań, PL
                Author information
                http://orcid.org/0000-0002-5390-0964
                Article
                10.5334/labphon.271
                bce9ae5a-7e50-4069-af29-521068d38bf8
                Copyright: © 2020 The Author(s)

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.

                History
                : 19 March 2020
                : 19 August 2020
                Categories
                Journal article

                Applied linguistics,General linguistics,Linguistics & Semiotics
                contextual frequency,Midland American English,t-glottaling

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