Dialects & our research

Regional language varieties in the north of the Netherlands

Dialects in the Netherlands are in decline, like most traditional dialects in Europe. The primary language for communication across the country is Standard Dutch, which has left little room for the traditional dialects spoken in the area. Dialects from different languages than Dutch, such as those of the Frisian and Low Saxon languages (blue and green on the map), are no exception to this rule.


The European Union recognizes the importance of protecting regional and minority languages since the 1990s. The European Charter for Regional or Minority Languages (ECRML) was constructed to protect the cultural heritage and linguistic identity represented by dialects. Member states were then invited to promote their non-standard language varieties under this Charter.


Nowadays, Frisian is recognized as a co-official language in Friesland and an official regional language under part III of the ECRML. The status of regional language also applies to Low Saxon and Limburgish dialects, although only under Part II.

Within this research project, we look at the phonetic patterns of Frisian and Low Saxon varieties from roughly the past 50 years. Throughout this time period, researchers have asked dialect speakers across the Netherlands to translate Dutch words into their local dialect. These studies have generated large datasets of phonetic data, which we analyze using computational techniques. By repeating these analyses over time, we obtain a picture of how dialects change. 

There are several questions that we are trying to answer at this moment (within smaller research projects), such as:

  • Are dialects changing towards Standard Dutch?
  • Are dialects changing towards neighboring dialects?
  • Is regiolect formation (i.e. supraregional dialects instead of local ones) taking place for Low Saxon?
  • How much data (i.e. words and/or locations) is necessary for reliable dialectometrical analyses?
  • What is the role of social factors, such as local population, age, language background, and language perception in dialect change?

A large part of my work is also dedicated to improving existing methodology for these type of analyses. Known problems with these type of data include:

  • Transcriber differences. Because we use data from different time periods and research projects, many different transcribers have contributed phonetic transcriptions. This poses an inherent problem of the data, because transcribers often disagree on phonetic transcriptions. This is due to different hearing systems and auditory perception, but also individual transcriber backgrounds. This problem can be solved by reducing the variation each transcriber introduces into the data. This is a unique and new approach, however, so it needs further validation within the field.
  • Variation within recording locations. For a considerable part of the data only one speaker from a geographical area was recorded, which naturally leaves it a possibility that our estimations are incorrect. This problem is less problematic if there are enough data per location and the dialect continuum is not sampled too sparsely.
  • Variation within speakers. Language systems are always changing on different levels due to complex social and linguistic dynamics. As we rely on at most a handful of speakers in each location, we want to estimate how much each individual changed during their lifetime. This ensures an accurate reflection of each individual contribution to our models. Most of our participants have already participated in a research project 10 years ago. By re-recording them now and letting them partake in phonetic convergence experiments, we can estimate these individual tendencies. These results will be invaluable to the field, but also to other fields where the apparent-time hypothesis is actively used. 

Besides our main research, we also busy ourselves within our lab with communicating our research to the dialect speaker population in new ways, such as:

  • The development and maintenance of a citizen science app (Stemmen van Groningen), which predicts where speakers are born based on their pronunciation of 10 simple words. Often, this turns out to be very accurate, but we are working on improving its accuracy.
  • A more general digitization effort is executed within the Woordwaark project (which includes Stemmen). The goal of this project is a large language database specifically for the Gronings dialect. This includes recording where which variants of Gronings are spoken, but also including as many dictionaries as possible.
  • The development of text-to-speech software for Low Saxon dialects (specifically by my colleague Wietse de Vries). This makes it possible to read aloud stories in the Gronings dialect (see this impressive example).
  • The development of a short teaching program of 10 weeks for primary schools in the province of Groningen. Within this program, we encourage children to do a small dialect research project and we introduce them to the local dialect. Furthermore, we employ a mobile app for learning Gronings (again developed by Wietse). The data for this app comes from the Van Old noar Jong project. Within this project, we ask older generations of dialect speakers to record the words and stories they would like to pass on to younger generations. The app is now available in the Google Playstore and Apple App Store.

Did you know?

There is a common misconception that languages stop at geographical borders, but this is rarely the case. For political reasons, we are often forced to pretend that geographically close language varieties differ greatly, but languages and dialects almost always gradually transition into each other’s areas.

In the Figure on the right side, for example, we can see the major language families in the Netherlands: Frisian in blue, Low Saxon in green, and Low Franconian in pink. When we sample these languages across the area, however, we see that our linguistic landscape is much more like a complex painting than a jigsaw puzzle. Move the slider and see for yourself!