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        <title>Massively Multilingual Neural Grapheme-to-Phoneme Conversion</title>
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        <namePart type="given">Ben</namePart>
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    <name type="personal">
        <namePart type="given">Josef</namePart>
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            <title>Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems</title>
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        <name type="personal">
            <namePart type="given">Emily</namePart>
            <namePart type="family">Bender</namePart>
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            <namePart type="given">Hal</namePart>
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            <namePart type="given">Sudha</namePart>
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    <abstract>Grapheme-to-phoneme conversion (g2p) is necessary for text-to-speech and automatic speech recognition systems. Most g2p systems are monolingual: they require language-specific data or handcrafting of rules. Such systems are difficult to extend to low resource languages, for which data and handcrafted rules are not available. As an alternative, we present a neural sequence-to-sequence approach to g2p which is trained on spelling–pronunciation pairs in hundreds of languages. The system shares a single encoder and decoder across all languages, allowing it to utilize the intrinsic similarities between different writing systems. We show an 11% improvement in phoneme error rate over an approach based on adapting high-resource monolingual g2p models to low-resource languages. Our model is also much more compact relative to previous approaches.</abstract>
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    <identifier type="doi">10.18653/v1/W17-5403</identifier>
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%0 Conference Proceedings
%T Massively Multilingual Neural Grapheme-to-Phoneme Conversion
%A Peters, Ben
%A Dehdari, Jon
%A van Genabith, Josef
%Y Bender, Emily
%Y Daumé III, Hal
%Y Ettinger, Allyson
%Y Rao, Sudha
%S Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F peters-etal-2017-massively
%X Grapheme-to-phoneme conversion (g2p) is necessary for text-to-speech and automatic speech recognition systems. Most g2p systems are monolingual: they require language-specific data or handcrafting of rules. Such systems are difficult to extend to low resource languages, for which data and handcrafted rules are not available. As an alternative, we present a neural sequence-to-sequence approach to g2p which is trained on spelling–pronunciation pairs in hundreds of languages. The system shares a single encoder and decoder across all languages, allowing it to utilize the intrinsic similarities between different writing systems. We show an 11% improvement in phoneme error rate over an approach based on adapting high-resource monolingual g2p models to low-resource languages. Our model is also much more compact relative to previous approaches.
%R 10.18653/v1/W17-5403
%U https://aclanthology.org/W17-5403
%U https://doi.org/10.18653/v1/W17-5403
%P 19-26

Markdown (Informal)

[Massively Multilingual Neural Grapheme-to-Phoneme Conversion](https://aclanthology.org/W17-5403) (Peters et al., 2017)

• Massively Multilingual Neural Grapheme-to-Phoneme Conversion (Peters et al., 2017)

ACL

• Ben Peters, Jon Dehdari, and Josef van Genabith. 2017. Massively Multilingual Neural Grapheme-to-Phoneme Conversion. In Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems, pages 19–26, Copenhagen, Denmark. Association for Computational Linguistics.