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      From categories to gradience: Auto-coding sociophonetic variation with random forests

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          Abstract

          The time-consuming nature of coding sociophonetic variables that are typically treated as categorical represents an impediment to addressing research questions around these variables that require large volumes of data. In this paper, we apply a machine learning method, random forest classification ( Breiman, 2001), to automate coding (categorical prediction) of two English sociophonetic variables traditionally treated as categorical, non-prevocalic /r/ and word-medial intervocalic /t/, based on tokens’ acoustic signatures. We found good performance for binary classifiers of non-prevocalic /r/ (Absent versus Present) and medial /t/ (Voiced versus Voiceless), but not for medial /t/ with a six-way coding distinction (largely due to some codes being sparsely represented in the training data). This method also yields rankings of acoustic measures in terms of importance in classification. Beyond any individual measures, this method generates probabilistic predictions of variation (classifier probabilities) that represent a composite of the acoustic cues fed into the model. In a listening experiment, we found that not only did classifier probabilities significantly capture gradience in trained listeners’ perceptions of rhoticity, they better predicted listeners’ perceptions than individual acoustic measures. This method thus represents a new approach to reconciling the categorical and continuous dimensions of sociophonetic variation.

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          Most cited references 42

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          Coda glottalization in American English

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            The Buckeye corpus of conversational speech: labeling conventions and a test of transcriber reliability

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              LaBB-CAT: An annotation store

               R. Fromont,  J Hay,  J. HAY (2012)
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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
                10 June 2020
                2020
                : 11
                : 1
                Affiliations
                [1 ]Department of Linguistics, University of Pittsburgh, Pittsburgh, PA, US
                [2 ]New Zealand Institute of Language, Brain and Behaviour, University of Canterbury, Christchurch, NZ
                [3 ]Department of Linguistics, University of Canterbury, Christchurch, NZ
                Article
                10.5334/labphon.216
                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/.

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