# Generating Feature Vectors from Phonetic Transcriptions in Cross-Linguistic Data Formats

arXiv: https://arxiv.org/abs/2405.04271 · HTML: https://mlsanity.com/paper/2405.04271/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 49 references that are in the map.
- First posted on arXiv 7 May 2024.

## Builds on (most cited first, 5 of 5)

- [Phonological Features for 0-shot Multilingual Speech Synthesis](https://mlsanity.com/paper/2008.04107/) (2020)
- [Language-Agnostic Meta-Learning for Low-Resource Text-to-Speech with Articulatory Features](https://mlsanity.com/paper/2203.03191/) (2022)
- [Ab Antiquo: Neural Proto-language Reconstruction](https://mlsanity.com/paper/1908.02477/) (2019)
- [Computational Historical Linguistics](https://mlsanity.com/paper/1805.08099/) (2018)
- [A Computational Model for the Assessment of Mutual Intelligibility Among Closely Related Languages](https://mlsanity.com/paper/2402.02915/) (2024)

## Built on by (most cited first, 4 of 4)

- [Unstable Grounds for Beautiful Trees? Testing the Robustness of Concept Translations in the Compilation of Multilingual Wordlists](https://mlsanity.com/paper/2503.00464/) (2025)
- [Advancing the Database of Cross-Linguistic Colexifications with New Workflows and Data](https://mlsanity.com/paper/2503.11377/) (2025)
- [A Graph Diffusion Algorithm for Lexical Similarity Evaluation](https://mlsanity.com/paper/2504.06816/) (2025)
- [Self-Supervised Lexical Representation Learning for Fast, Large-Scale Phylogenetic Inference](https://mlsanity.com/paper/2609.05262/) (2026)

## Abstract (arXiv)

When comparing speech sounds across languages, scholars often make use of feature representations of individual sounds in order to determine fine-grained sound similarities. Although binary feature systems for large numbers of speech sounds have been proposed, large-scale computational applications often face the challenges that the proposed feature systems -- even if they list features for several thousand sounds -- only cover a smaller part of the numerous speech sounds reflected in actual cross-linguistic data. In order to address the problem of missing data for attested speech sounds, we propose a new approach that can create binary feature vectors dynamically for all sounds that can be represented in the the standardized version of the International Phonetic Alphabet proposed by the Cross-Linguistic Transcription Systems (CLTS) reference catalog. Since CLTS is actively used in large data collections, covering more than 2,000 distinct language varieties, our procedure for the generation of binary feature vectors provides immediate access to a very large collection of multilingual wordlists. Testing our feature system in different ways on different datasets proves that the system is not only useful to provide a straightforward means to compare the similarity of speech sounds, but also illustrates its potential to be used in future cross-linguistic machine learning applications.