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        <title>G&amp;P2P: A Multi-Source Approach to Grapheme-to-Phoneme Conversion</title>
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        <namePart type="given">Chun-Yi</namePart>
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            <title>Proceedings of the Third Workshop on Computation and Written Language (CAWL 2026) @ LREC 2026</title>
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            <namePart type="given">Kyle</namePart>
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    <abstract>Grapheme-to-phoneme (G2P) conversion plays a central role in speech technologies. This paper introduces G&amp;P2P, a multi-source framework that integrates multiple pronunciation dictionaries to enhance G2P modeling. We evaluate both expert-curated and crowd-sourced resources using attentive LSTM, pointer-generator LSTM, and transformer architectures. Results indicate that combining high-quality expert dictionaries yields substantial improvements, achieving an 11.26-point absolute (22% relative) reduction in word error rate. In contrast, incorporating noisy crowd-sourced resources may degrade performance. Statistical analyses further suggest that dataset quality exerts a greater influence on outcomes than the choice of fusion strategy, offering practical guidance for the design of multi-source G2P systems.</abstract>
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%0 Conference Proceedings
%T G&P2P: A Multi-Source Approach to Grapheme-to-Phoneme Conversion
%A Peng, Chun-Yi Jerry
%Y Gorman, Kyle
%S Proceedings of the Third Workshop on Computation and Written Language (CAWL 2026) @ LREC 2026
%D 2026
%8 June
%I ELRA Language Resources Association (ELRA)
%C Palma de Mallorca, Spain
%F peng-2026-g
%X Grapheme-to-phoneme (G2P) conversion plays a central role in speech technologies. This paper introduces G&P2P, a multi-source framework that integrates multiple pronunciation dictionaries to enhance G2P modeling. We evaluate both expert-curated and crowd-sourced resources using attentive LSTM, pointer-generator LSTM, and transformer architectures. Results indicate that combining high-quality expert dictionaries yields substantial improvements, achieving an 11.26-point absolute (22% relative) reduction in word error rate. In contrast, incorporating noisy crowd-sourced resources may degrade performance. Statistical analyses further suggest that dataset quality exerts a greater influence on outcomes than the choice of fusion strategy, offering practical guidance for the design of multi-source G2P systems.
%R 10.63317/4f2x3fda6jj7
%U https://aclanthology.org/2026.cawl-1.9/
%U https://doi.org/10.63317/4f2x3fda6jj7
%P 89-94

Markdown (Informal)

[G&P2P: A Multi-Source Approach to Grapheme-to-Phoneme Conversion](https://aclanthology.org/2026.cawl-1.9/) (Peng, CAWL 2026)

• G&P2P: A Multi-Source Approach to Grapheme-to-Phoneme Conversion (Peng, CAWL 2026)

ACL

• Chun-Yi Jerry Peng. 2026. G&P2P: A Multi-Source Approach to Grapheme-to-Phoneme Conversion. In Proceedings of the Third Workshop on Computation and Written Language (CAWL 2026) @ LREC 2026, pages 89–94, Palma de Mallorca, Spain. ELRA Language Resources Association (ELRA).