A cross-linguistic database of phonetic transcription systems

Abstract

Contrary to what non-practitioners might expect, the systems of phonetic notation used by linguists are highly idiosyncratic. Not only do various linguistic subfields disagree on the specific symbols they use to denote the speech sounds of languages, but also in large databases of sound inventories considerable variation can be found. Inspired by recent efforts to link cross-linguistic data with help of reference catalogues (Glottolog, Concepticon) across different resources, we present initial efforts to link different phonetic notation systems to a catalogue of speech sounds. This is achieved with the help of a database accompanied by a software framework that uses a limited but easily extendable set of non-binary feature values to allow for quick and convenient registration of different transcription systems, while at the same time linking to additional datasets with restricted inventories. Linking different transcription systems enables us to conveniently translate between different phonetic transcription systems, while linking sounds to databases allows users quick access to various kinds of metadata, including feature values, statistics on phoneme inventories, and information on prosody and sound classes. In order to prove the feasibility of this enterprise, we supplement an initial version of our cross-linguistic database of phonetic transcription systems (CLTS), which currently registers five transcription systems and links to fifteen datasets, as well as a web application, which permits users to conveniently test the power of the automatic translation across transcription systems.

The Database of Cross-Linguistic Colexifications, reproducible analysis of cross-linguistic polysemies

“…Along with the growing amount of digitally available data for the world's languages, we find a substantial increase in the application of new quantitative techniques. While most of the new methods are inspired by neighboring disciplines and general-purpose frameworks, such as evolutionary biology 1,2 , machine learning 3,4 , or statistical modeling 5,6 , the particularities of cross-linguistic data often necessitate a specific treatment of materials (reflected in recent standardization efforts 7,8 ) and methods (illustrated by the development of new algorithms tackling specifically linguistic problems 9,10 ).…”

Automated phylogeny of Palaung dialects

“…First, to tokenize the data (split it up into sound segments), an orthography profile, as outlined in Wu et al (2020), was used by the Cross-Linguistic Data Formats Bench (Forkel and List, 2020) on the raw data. The CLDF Bench uses CLTS, or Cross-Linguistic Transcription Systems (Anderson et al, 2018), to consolidate transcriptions of words done by different linguists.…”

Using lexical language models to detect borrowings in monolingual wordlists

“…Since translations may lack or one concept may have been represented by more than one word form, the resulting wordlists comprise between 956 and 2,558 word forms. While word forms were provided in orthographic form or phonological transcriptions in the original data, we added phonetic transcriptions which follow the unified Broad IPA transcription system proposed by the Cross-Linguistic Transcription Systems reference catalog [ 33 , 34 ] with the help of orthography profiles [ 35 ] manually compiled by reading the relevant literature for each language. Orthography profiles can be best thought of as a specific look-up table, which allows to convert transcriptions from one orthography into another one (compare the presentation in Wu et al [ 36 ] for details); while such assisted transcription can introduce noise in the data, no comparable lexical database with transcriptions and loanword annotation was available.…”