Seminar für Sprachwissenschaft

As Quantitative Linguistics group there are several projects we are currently working on. A short description of the projects associated with our group follows. For a more detailed view of the research ideas we are investigating look at  Harald Baayens website. Projects that have been finished or run out of funding can be found in Previous Projects.



Wide Incremental learning with Discrimination nEtworks

Principal Investigator: R. Harald Baayen (Professor of Quantitative Linguistics)



Details to ERC-WIDE

Project aims

This five-year project aims to deepen our understanding of how we produce and understand words in everyday speech.

Words in day-to-day conversational speech may differ substantially from how they appear in writing: German "würden" is often pronounced as "wün," Dutch "natuurlijk" ('naturally') can reduce to "tk", and Mandarin 要不然 (jao pu zan, 'otherwise') to “ui." Current theories assume that the sound waves that reach our ears are reduced to sequences of abstract sound units, much like the sequences of letters that make up written words. However, how to align highly reduced forms such as "wün", "tk" and "ui" with their full unreduced variants, the supposed gatekeepers to meaning, is an unsolved computational problem.

The WIDE project makes the radical proposal to eliminate letter-like sound units altogether, and instead to zoom in on the rich details of the speech signal itself. Given tens of thousands of smart features representing the richness of the speech signal, it is anticipated that artificial neural networks can learn, by trial and error, to identify which meanings are conveyed. Previous research funded by the Alexander von Humboldt foundation allowed to provide a first proof of concept. In the WIDE project, this approach will be developed further and extended from German to other languages, including Mandarin Chinese (a tone language) and Estonian (a complex language with 28 to 40 different forms for a given noun). The WIDE project also targets a computational model without sound units for the articulation of words in speech production.

The project's name, "WIDE", highlights a second aspect in which this project makes a radical departure from current trends in linguistics and natural language processing. Instead of making use of deep learning networks, the project focuses on the potential of ‘wide' two-layer networks with tens of thousands of input and output units.


  • R. Harald Baayen (Professor, Principal Investigator)

  • Yu-Ying Chuang (Postdoc)

  • Maja Linke (PhD)

  • Jessie Nixon (Postdoc)

  • Maria Heitmeier (PhD)

  • Tino Sering (PhD)

  • Elnaz Shafaei Bajestan (PhD)

  • Kun Sun (Postdoc)


Spoken Morphology: Phonetics and phonology of complex words

DFG Research Unit FOR 2373 (Director: Prof. Dr. Ingo Plag)



Details to DFG-ART

Sub-project ART: The articulation of morphologically complex words

ART is a subproject of the research unit „Spoken Morphology: Phonetics and phonology of complex words“ funded by the Deutsche Forschungsgemeinschaft (DFG) that investigates the articulation of morphologically complex words with the help of electromagnetic articulography.


  • R. Harald Baayen (Principal Investigator)
  • Benjamin V. Tucker (Mercator Fellow)
  • Fabian Tomaschek (Postdoc)
  • Motoki Saito (Research assistant)


Machine Learning for Science

Cluster of Excellence - Machine Learning for Science (Cluster speaker: Philipp Berens, Cluster speaker: Ulrike von Luxburg)


Details to DFG-EML

Innovation Fund Project 1 in research area A - Beyond Prediction, Towards Understanding

In research area A, we will design algorithms that reveal complex structure and causal relationships from data in order to integrate machine learning into the scientific discovery process. Project 1 investigates "Enhancing Machine Learning of Lexical Semantics with Image Mining".


  • Hendrik Lensch (Principal investigator)
  • R. Harald Baayen (Principal investigator)
  • Zohreh Ghaderi (Phd student)
  • Hassan Shahmohammadi (Phd student)