Work

IPA CHILDES & G2P+: Feature-Rich Resources for Cross-Lingual Phonology and Phonemic Language Modeling

Year: 2025

Type: conference-paper

Abstract: In this paper, we introduce two resources: (i) G2P+, a tool for converting orthographic datasets to a consistent phonemic representation; and (ii) IPA CHILDES, a phonemic dataset of child-directed and child-produced speech across 31 languages.Prior tools for grapheme-to-phoneme conversion result in phonemic vocabularies that are inconsistent with established phonemic inventories, an issue which G2P+ addresses by leveraging the inventories in the Phoible database (Moran and McCloy, 2019).Using this tool, we augment CHILDES (MacWhinney and Snow, 1985) with phonemic transcriptions to produce IPA CHILDES.This new resource fills several gaps in existing phonemic datasets, which often lack multilingual coverage, spontaneous speech, and a focus on child-directed language.We demonstrate the utility of this dataset for phonological research by training phoneme language models on 11 languages and probing them for distinctive features, finding that the distributional properties of phonemes are sufficient to learn major class and place features cross-lingually.phonemetransformers/ipa-childes (CC BY 4.0) codebyzeb/g2p-plus (MIT)

Source: Conference on Computational Natural Language Learning

DOI: 10.18653/v1/2025.conll-1.33

Authors: Zebulon Goriely, Paula Buttery

Institution: University of Cambridge

Language: English

Topic: Phonetics and Phonology Research

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: hybrid

Funders: Dell EMC, Engineering and Physical Sciences Research Council, Science and Technology Facilities Council

Awards: G581313191, Cambridge Service for Data Driven Discovery (CSD3) - A National Data Intensive Science Cloud for Converged Simulation, AI & Analytics