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    <titleInfo>
        <title>Deep Learning and Sociophonetics: Automatic Coding of Rhoticity Using Neural Networks</title>
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    <name type="personal">
        <namePart type="given">Sarah</namePart>
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            <namePart type="given">Sudipta</namePart>
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            <namePart type="given">Farah</namePart>
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            <namePart type="given">Laura</namePart>
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        <name type="personal">
            <namePart type="given">Na-Rae</namePart>
            <namePart type="family">Han</namePart>
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    <abstract>Automated extraction methods are widely available for vowels, but automated methods for coding rhoticity have lagged far behind. R-fulness versus r-lessness (in words like park, store, etc.) is a classic and frequently cited variable, but it is still commonly coded by human analysts rather than automated methods. Human-coding requires extensive resources and lacks replicability, making it difficult to compare large datasets across research groups. Can reliable automated methods be developed to aid in coding rhoticity? In this study, we use Neural Networks/Deep Learning, training our model on 208 Boston-area speakers.</abstract>
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    <identifier type="doi">10.18653/v1/N19-3013</identifier>
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%0 Conference Proceedings
%T Deep Learning and Sociophonetics: Automatic Coding of Rhoticity Using Neural Networks
%A Gupta, Sarah
%A DiPadova, Anthony
%Y Kar, Sudipta
%Y Nadeem, Farah
%Y Burdick, Laura
%Y Durrett, Greg
%Y Han, Na-Rae
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F gupta-dipadova-2019-deep
%X Automated extraction methods are widely available for vowels, but automated methods for coding rhoticity have lagged far behind. R-fulness versus r-lessness (in words like park, store, etc.) is a classic and frequently cited variable, but it is still commonly coded by human analysts rather than automated methods. Human-coding requires extensive resources and lacks replicability, making it difficult to compare large datasets across research groups. Can reliable automated methods be developed to aid in coding rhoticity? In this study, we use Neural Networks/Deep Learning, training our model on 208 Boston-area speakers.
%R 10.18653/v1/N19-3013
%U https://aclanthology.org/N19-3013
%U https://doi.org/10.18653/v1/N19-3013
%P 92-96

Markdown (Informal)

[Deep Learning and Sociophonetics: Automatic Coding of Rhoticity Using Neural Networks](https://aclanthology.org/N19-3013) (Gupta & DiPadova, NAACL 2019)

• Deep Learning and Sociophonetics: Automatic Coding of Rhoticity Using Neural Networks (Gupta & DiPadova, NAACL 2019)

ACL

• Sarah Gupta and Anthony DiPadova. 2019. Deep Learning and Sociophonetics: Automatic Coding of Rhoticity Using Neural Networks. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 92–96, Minneapolis, Minnesota. Association for Computational Linguistics.