---
title: "IPA CHILDES & G2P+ — Goriely 2025"
description: "The authors propose two resources. 94 references, cited by 6 papers here."
authors: ["Zébulon Goriely", "Paula Buttery"]
year: 2025
venue: "Proceedings of the 29th Conference on Computational Natural Language Learning"
source: "https://aclanthology.org/2025.conll-1.33/"
doi: "10.18653/v1/2025.conll-1.33"
arxiv: "2504.03036"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/goriely-2025-ipa-childes-g2p-feature-rich/"
---
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# IPA CHILDES & G2P+: Feature-Rich Resources for Cross-Lingual Phonology and Phonemic Language Modeling

**Authors:** Zébulon Goriely and [Paula Buttery](https://md.research.elamur.ai/free/en/authors/paula-buttery/) · **Year:** 2025 · **Venue:** Proceedings of the 29th Conference on Computational Natural Language Learning · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.conll-1.33/) · [PDF](https://aclanthology.org/2025.conll-1.33.pdf) · [DOI 10.18653/v1/2025.conll-1.33](https://doi.org/10.18653/v1/2025.conll-1.33) · [arXiv 2504.03036](https://arxiv.org/abs/2504.03036)

**Citations:** 6 in our library, of the 140,884 papers whose bibliographies we read

## Summary

Large-scale phonological studies lack phonemic data. Automatic G2P tools produce phoneme sets that are consistent neither with one another nor with established inventories. Multilingual phonemic corpora of spontaneous speech, in particular of speech by children and the adults who address them, are almost nonexistent. The authors propose two resources. The first is G2P+. It takes the output of existing G2P systems (phonemizer, epitran and others) and maps it onto the phoneme inventories of the Phoible database. As a result, transcriptions are identical regardless of the chosen backend, and every phoneme has a description in terms of distinctive features. The second resource is IPA CHILDES: using G2P+, the authors converted most of CHILDES into phonemic transcription for 31 languages. To demonstrate the corpus's usefulness, they trained monolingual phoneme-level language models for 11 languages. They then used linear probes to test whether the contextual phoneme embeddings encode their phonological features. It turned out that the distribution of phonemes in text alone suffices for the models in all languages to separate vowels from consonants and to learn manner and place of articulation features. The resources are open and suitable for research on language acquisition, cross-linguistic phonology and models for low-resource languages.

## Key points

- IPA CHILDES covers 31 languages and about 45 million words. According to the authors, it is the first phonemic corpus of speech by children and the adults who address them, and the first multilingual phonemic corpus of spontaneous speech. The corpus is distributed under CC BY 4.0 (phonemetransformers/ipa-childes), and G2P+ under MIT (codebyzeb/g2p-plus).
- The data source is CHILDES: over 1.4 TB of transcripts in more than 40 languages. Previously these transcripts were mostly orthographic, and the phonetic transcription in PhonBank covers only certain corpora and only children's utterances.
- For French, phonemizer and epitran produce noticeably different phoneme sets. G2P+ maps both onto the Phoible inventory.
- The phoneme-level language models were trained on the 11 largest languages in the corpus, roughly 500 thousand words per language, with embeddings of dimension 256. The probes perform best on the features syllabic and consonantal, while they barely capture delayedRelease.
- In the dendrograms for Japanese and French, vowels are almost completely separated from consonants (the exception being Japanese n). Overall, however, the grouping of phonemes in the models does not match the traditional articulatory classification.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · [🌍 Multilingual & cross-lingual NLP](https://md.research.elamur.ai/free/en/topics/multilingual-nlp/)

## Cite

Cite as: Zébulon Goriely and Paula Buttery. 2025. IPA CHILDES & G2P+: Feature-Rich Resources for Cross-Lingual Phonology and Phonemic Language Modeling. Proceedings of the 29th Conference on Computational Natural Language Learning.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{goriely2025ipa,
  title = {{IPA CHILDES \& G2P+: Feature-Rich Resources for Cross-Lingual Phonology and Phonemic Language Modeling}},
  author = {Zébulon Goriely and Paula Buttery},
  booktitle = {Proceedings of the 29th Conference on Computational Natural Language Learning},
  year = {2025},
  doi = {10.18653/v1/2025.conll-1.33},
  eprint = {2504.03036},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2025.conll-1.33}
}
```

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