Computer-Assisted Language Comparison in Practice

Preparing Acoustic Pitch Data for Computational Analysis and Presentation

2024-09-24T11:10:42+00:00

Pitch plays an important role in many linguistic systems. It is the primary set of features which determine vowel quality distinctions as well as forming the basis for intonation and contrastive tone systems. Unfortunately, much of the literature has relied on approaches to presenting and analysing pitch data that can result in a lack of data transparency, reproducibility, and analytical robustness. These issues are easily solved through the selection of a more appropriate scale for pitch values. This study presents the issues with using raw pitch data as Hertz values some historical efforts to resolve these issues, and two more appropriate solutions than some of the more widely used systems, with a way to easily calculate these alternative systems in a short Python script.

Generating Phonological Feature Vectors with SoundVectors and CLTS

2024-07-30T12:36:35+00:00

The recently published Python library soundvectors offers a simple and robust method to derive phonological feature vectors for any valid IPA sound via its canonical description. It is designed to interact neatly with the Cross-Linguistic Transcription Systems reference catalog (CLTS), which dynamically parses valid strings in phonetic transcription to describe speech sounds. This study illustrates how both systems can be used together to generate phonological feature vectors for all kinds of sounds without relying on a previously defined lookup table. Additionally, it compares the generated feature vectors with those obtained from two other prominent databases, PanPhon and PHOIBLE, showing how those systems can be accessed from the CLTS data via its Python API pyclts.

Using CLDFBench and PyLexibank on Windows

2024-12-16T12:38:01+00:00

Using tools such as CLDFBench and PyLexibank, datasets can be converted into Cross-Linguistic Data Formats (CLDF), offering a standardized and interoperable representation of linguistic data. While these tools are powerful, lifting datasets to CLDF can present unique challenges for Windows users due to idiosyncrasies in the Windows operating system. Although CLDFBench and PyLexibank are compatible with Windows, certain workarounds may be necessary to address system-specific issues. This guide aims to demonstrate how CLDFBench and PyLexibank can be effectively installed and used on a Windows computer to lift a dataset to CLDF.

Typing Special Characters as a Key Skill for Linguists

2024-10-29T10:27:50+00:00

Most linguists have to type special characters that are not available on an ordinary keyboard on a regular basis. Reflecting about the general problems involved in typing special characters, I review different solutions and argue that linguists should not only be able to type special characters on their computers, but that they should also have some basic knowledge about their technical aspects and know how to expand and customize them. In order to improve the training of young scholars, it is important to discuss special character typing more openly in linguistics, especially in the classroom and with doctoral students, sharing individual solutions openly.

Adding Standardized Transcriptions to Panoan and Tacanan Languages in the Intercontinental Dictionary Series

2024-08-07T06:22:58+00:00

In this study, we illustrate how standardized phonetic transcriptions can be added to the data for Panoan and Tacanan languages provided by the Intercontinental Dictionary Series. The result is presented as a new dataset that keeps reference to the original data and adds phonetic transcriptions for each word form in Panoan languages, Tacanan languages, as well as Spanish and Portuguese.

Converting an Artificial Proto-Language into Data for Testing Computational Approaches in Historical Linguistics

2024-07-14T12:58:39+00:00

This small study shows how data for an artificially created language that was supposed to reflect features of "proto-languages", predating modern languages by several thousand years, can be used in testing computational approaches in historical linguistics. In order to do so, computational workflow is described that retrieves the data automatically, creating a comparative wordlist compatible in format with software tools for historical linguistics, and then uses a baseline method for automatic cognate detection to compare an artificial language against a sample of Indo-European languages. The results show that artificial languages might help to fill a gap in testing that has so far been ignored in the literature.