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        <title>Multimodal neural pronunciation modeling for spoken languages with logographic origin</title>
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    <name type="personal">
        <namePart type="given">Minh</namePart>
        <namePart type="family">Nguyen</namePart>
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    <name type="personal">
        <namePart type="given">Gia</namePart>
        <namePart type="given">H</namePart>
        <namePart type="family">Ngo</namePart>
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    <name type="personal">
        <namePart type="given">Nancy</namePart>
        <namePart type="family">Chen</namePart>
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        <dateIssued>2018-oct-nov</dateIssued>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Chiang</namePart>
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            <namePart type="given">Jun’ichi</namePart>
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    <abstract>Graphemes of most languages encode pronunciation, though some are more explicit than others. Languages like Spanish have a straightforward mapping between its graphemes and phonemes, while this mapping is more convoluted for languages like English. Spoken languages such as Cantonese present even more challenges in pronunciation modeling: (1) they do not have a standard written form, (2) the closest graphemic origins are logographic Han characters, of which only a subset of these logographic characters implicitly encodes pronunciation. In this work, we propose a multimodal approach to predict the pronunciation of Cantonese logographic characters, using neural networks with a geometric representation of logographs and pronunciation of cognates in historically related languages. The proposed framework improves performance by 18.1% and 25.0% respective to unimodal and multimodal baselines.</abstract>
    <identifier type="citekey">nguyen-etal-2018-multimodal</identifier>
    <identifier type="doi">10.18653/v1/D18-1320</identifier>
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        <url>https://aclanthology.org/D18-1320</url>
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    <part>
        <date>2018-oct-nov</date>
        <extent unit="page">
            <start>2916</start>
            <end>2922</end>
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%0 Conference Proceedings
%T Multimodal neural pronunciation modeling for spoken languages with logographic origin
%A Nguyen, Minh
%A Ngo, Gia H.
%A Chen, Nancy
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F nguyen-etal-2018-multimodal
%X Graphemes of most languages encode pronunciation, though some are more explicit than others. Languages like Spanish have a straightforward mapping between its graphemes and phonemes, while this mapping is more convoluted for languages like English. Spoken languages such as Cantonese present even more challenges in pronunciation modeling: (1) they do not have a standard written form, (2) the closest graphemic origins are logographic Han characters, of which only a subset of these logographic characters implicitly encodes pronunciation. In this work, we propose a multimodal approach to predict the pronunciation of Cantonese logographic characters, using neural networks with a geometric representation of logographs and pronunciation of cognates in historically related languages. The proposed framework improves performance by 18.1% and 25.0% respective to unimodal and multimodal baselines.
%R 10.18653/v1/D18-1320
%U https://aclanthology.org/D18-1320
%U https://doi.org/10.18653/v1/D18-1320
%P 2916-2922

Markdown (Informal)

[Multimodal neural pronunciation modeling for spoken languages with logographic origin](https://aclanthology.org/D18-1320) (Nguyen et al., EMNLP 2018)

• Multimodal neural pronunciation modeling for spoken languages with logographic origin (Nguyen et al., EMNLP 2018)

ACL

• Minh Nguyen, Gia H. Ngo, and Nancy Chen. 2018. Multimodal neural pronunciation modeling for spoken languages with logographic origin. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2916–2922, Brussels, Belgium. Association for Computational Linguistics.