# A Corpus for Large-Scale Phonetic Typology

arXiv: https://arxiv.org/abs/2005.13962 · HTML: https://mlsanity.com/paper/2005.13962/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2020.

## Built on by (most cited first, 9 of 9)

- [A surprisal--duration trade-off across and within the world's languages](https://mlsanity.com/paper/2109.15000/) (2021)
- [ZIPA: A family of efficient models for multilingual phone recognition](https://mlsanity.com/paper/2505.23170/) (2025)
- [The taste of IPA: Towards open-vocabulary keyword spotting and forced alignment in any language](https://mlsanity.com/paper/2311.08323/) (2023)
- [Using Linguistic Typology to Enrich Multilingual Lexicons: the Case of Lexical Gaps in Kinship](https://mlsanity.com/paper/2204.05049/) (2022)
- [Phonetic Segmentation of the UCLA Phonetics Lab Archive](https://mlsanity.com/paper/2403.19509/) (2024)
- [IPA-CHILDES & G2P+: Feature-Rich Resources for Cross-Lingual Phonology and Phonemic Language Modeling](https://mlsanity.com/paper/2504.03036/) (2025)
- [Lexical Diversity in Kinship Across Languages and Dialects](https://mlsanity.com/paper/2308.13056/) (2023)
- [Colexifications for Bootstrapping Cross-lingual Datasets: The Case of Phonology, Concreteness, and Affectiveness](https://mlsanity.com/paper/2306.02646/) (2023)
- [Efficiently Identifying Low-Quality Language Subsets in Multilingual Datasets: A Case Study on a Large-Scale Multilingual Audio Dataset](https://mlsanity.com/paper/2410.04292/) (2024)

## Abstract (arXiv)

A major hurdle in data-driven research on typology is having sufficient data in many languages to draw meaningful conclusions. We present VoxClamantis v1.0, the first large-scale corpus for phonetic typology, with aligned segments and estimated phoneme-level labels in 690 readings spanning 635 languages, along with acoustic-phonetic measures of vowels and sibilants. Access to such data can greatly facilitate investigation of phonetic typology at a large scale and across many languages. However, it is non-trivial and computationally intensive to obtain such alignments for hundreds of languages, many of which have few to no resources presently available. We describe the methodology to create our corpus, discuss caveats with current methods and their impact on the utility of this data, and illustrate possible research directions through a series of case studies on the 48 highest-quality readings. Our corpus and scripts are publicly available for non-commercial use at https://voxclamantisproject.github.io.