Generating Feature Vectors from Phonetic Transcriptions in Cross-Linguistic Data Formats
This provides a solution for linguists and computational researchers working with cross-linguistic phonetic data, though it is incremental as it builds on existing feature systems and standards.
The paper tackles the problem of missing binary feature representations for many attested speech sounds in cross-linguistic data by proposing a method to dynamically generate feature vectors for sounds in the CLTS reference catalog, enabling access to over 2,000 language varieties and demonstrating utility for sound similarity comparisons and future machine learning applications.
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.