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

TL;DR

This paper introduces a dynamic method to generate binary feature vectors for speech sounds from the International Phonetic Alphabet, enabling large-scale cross-linguistic comparisons and machine learning applications.

Contribution

It presents a novel approach to create comprehensive feature vectors for all sounds in the CLTS standard, addressing missing data issues in cross-linguistic phonetic analysis.

Findings

Effective comparison of speech sound similarities across languages.

Supports large multilingual datasets with over 2,000 language varieties.

Potential to enhance cross-linguistic machine learning applications.

Abstract

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.