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        <title>Predicting Historical Phonetic Features using Deep Neural Networks: A Case Study of the Phonetic System of Proto-Indo-European</title>
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            <title>Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change</title>
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            <namePart type="given">Nina</namePart>
            <namePart type="family">Tahmasebi</namePart>
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        <name type="personal">
            <namePart type="given">Lars</namePart>
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        <name type="personal">
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        <name type="personal">
            <namePart type="given">Yang</namePart>
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    <abstract>Traditional historical linguistics lacks the possibility to empirically assess its assumptions regarding the phonetic systems of past languages and language stages since most current methods rely on comparative tools to gain insights into phonetic features of sounds in proto- or ancestor languages. The paper at hand presents a computational method based on deep neural networks to predict phonetic features of historical sounds where the exact quality is unknown and to test the overall coherence of reconstructed historical phonetic features. The method utilizes the principles of coarticulation, local predictability and statistical phonological constraints to predict phonetic features by the features of their immediate phonetic environment. The validity of this method will be assessed using New High German phonetic data and its specific application to diachronic linguistics will be demonstrated in a case study of the phonetic system Proto-Indo-European.</abstract>
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%0 Conference Proceedings
%T Predicting Historical Phonetic Features using Deep Neural Networks: A Case Study of the Phonetic System of Proto-Indo-European
%A Hartmann, Frederik
%Y Tahmasebi, Nina
%Y Borin, Lars
%Y Jatowt, Adam
%Y Xu, Yang
%S Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F hartmann-2019-predicting
%X Traditional historical linguistics lacks the possibility to empirically assess its assumptions regarding the phonetic systems of past languages and language stages since most current methods rely on comparative tools to gain insights into phonetic features of sounds in proto- or ancestor languages. The paper at hand presents a computational method based on deep neural networks to predict phonetic features of historical sounds where the exact quality is unknown and to test the overall coherence of reconstructed historical phonetic features. The method utilizes the principles of coarticulation, local predictability and statistical phonological constraints to predict phonetic features by the features of their immediate phonetic environment. The validity of this method will be assessed using New High German phonetic data and its specific application to diachronic linguistics will be demonstrated in a case study of the phonetic system Proto-Indo-European.
%R 10.18653/v1/W19-4713
%U https://aclanthology.org/W19-4713
%U https://doi.org/10.18653/v1/W19-4713
%P 98-108

Markdown (Informal)

[Predicting Historical Phonetic Features using Deep Neural Networks: A Case Study of the Phonetic System of Proto-Indo-European](https://aclanthology.org/W19-4713) (Hartmann, LChange 2019)

• Predicting Historical Phonetic Features using Deep Neural Networks: A Case Study of the Phonetic System of Proto-Indo-European (Hartmann, LChange 2019)

ACL

• Frederik Hartmann. 2019. Predicting Historical Phonetic Features using Deep Neural Networks: A Case Study of the Phonetic System of Proto-Indo-European. In Proceedings of the 1st International Workshop on Computational Approaches to Historical Language Change, pages 98–108, Florence, Italy. Association for Computational Linguistics.