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        <title>Low-resource grapheme-to-phoneme mapping with phonetically-conditioned transfer</title>
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            <title>Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology</title>
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            <namePart type="given">Garrett</namePart>
            <namePart type="family">Nicolai</namePart>
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        <name type="personal">
            <namePart type="given">Eleanor</namePart>
            <namePart type="family">Chodroff</namePart>
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        <name type="personal">
            <namePart type="given">Frederic</namePart>
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    <abstract>In this paper we explore a very simple nonneural approach to mapping orthography to phonetic transcription in a low-resource context with transfer data from a related language. We start from a baseline system and focus our efforts on data augmentation. We make three principal moves. First, we start with an HMMbased system (Novak et al., 2012). Second, we augment our basic system by recombining legal substrings in restricted fashion (Ryan and Hulden, 2020). Finally, we limit our transfer data by only using training pairs where the phonetic form shares all bigrams with the target language.</abstract>
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    <identifier type="doi">10.18653/v1/2023.sigmorphon-1.29</identifier>
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%0 Conference Proceedings
%T Low-resource grapheme-to-phoneme mapping with phonetically-conditioned transfer
%A Hammond, Michael
%Y Nicolai, Garrett
%Y Chodroff, Eleanor
%Y Mailhot, Frederic
%Y Çöltekin, Çağrı
%S Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F hammond-2023-low
%X In this paper we explore a very simple nonneural approach to mapping orthography to phonetic transcription in a low-resource context with transfer data from a related language. We start from a baseline system and focus our efforts on data augmentation. We make three principal moves. First, we start with an HMMbased system (Novak et al., 2012). Second, we augment our basic system by recombining legal substrings in restricted fashion (Ryan and Hulden, 2020). Finally, we limit our transfer data by only using training pairs where the phonetic form shares all bigrams with the target language.
%R 10.18653/v1/2023.sigmorphon-1.29
%U https://aclanthology.org/2023.sigmorphon-1.29
%U https://doi.org/10.18653/v1/2023.sigmorphon-1.29
%P 245-248

Markdown (Informal)

[Low-resource grapheme-to-phoneme mapping with phonetically-conditioned transfer](https://aclanthology.org/2023.sigmorphon-1.29) (Hammond, SIGMORPHON 2023)

• Low-resource grapheme-to-phoneme mapping with phonetically-conditioned transfer (Hammond, SIGMORPHON 2023)

ACL

• Michael Hammond. 2023. Low-resource grapheme-to-phoneme mapping with phonetically-conditioned transfer. In Proceedings of the 20th SIGMORPHON workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 245–248, Toronto, Canada. Association for Computational Linguistics.