Taal en Tongval - Volume 75, Issue 1, 2023

Large phonetic corpora are frequently used to investigate language variation and change in dialects, but these corpora are often constructed by many researchers in a collaborative effort. This typically results in inter-transcriber issues that may impact the reliability of analyses using these data. This problem is exacerbated when multiple phonetic corpora are compared when investigating real time dialect change. In this study, we therefore propose a method to automatically and iteratively merge phonetic symbols used in the transcriptions to obtain a more coarse-grained, but better comparable, phonetic transcription. Our approach is evaluated using two large phonetic Netherlandic dialect corpora in an attempt to estimate sound change in the area in the 20th century. The results are discussed in the context of the available literature about dialect change in the Netherlandic area.

Recent work in dialectometry has proposed the use of linear mixed-effects regression (LMER) for analysing full distance matrices. While the outcomes are promising, work is needed to confirm that such outcomes are valid, given that the analysis of distance matrices using this method is not established. The current contribution provides a supporting framework for this approach by testing its validity through a series of simulated datasets. We analysed the generated data using LMER, and compared its performance to that of the well-established multiple regression on distance matrices (MRM) approach. We find that the LMER results are on par with—and sometimes even exceed—the results obtained from MRM. The potential to include random effects makes LMER a more powerful tool than MRM to examine a linguistic area as a whole, with all pairwise comparisons included, making it an ideal candidate for big data analyses that are becoming more prevalent with the ongoing digitisation of large dialect databases.