Seminar für Sprachwissenschaft

We have just published our outreach article in the leading science communication publication, Scientia:

Baayen, R. H. (2019). Are You Listening? Teaching a Machine to Understand Speech. Scientia, 2019, 1-5. doi


My current central research interests include the following. 

Computational modeling of lexical processing.  

Funded by the European research council, we are investigating the potential of wide learning (modeling with huge linear networks) for understanding human lexical processing in reading, listening, and speaking.  We have recently provided a proof of concept that the processing of both simple and morphologically complex words can be achieved with very high accuracy without requiring theoretical constructs such as morphemes, stems, exponents, inflectional classes, and exceptions.  Our new model, Linear Discriminative
Learning, is a formalization of Word and Paradigm Morphology (Blevins, 2016, CUP) that is grounded in discrimination learning.  A detailed study of English inflectional and derivational morphology is provided in Baayen, Chuang, Shafaei-Bajestan and Blevins (2018, Complexity) and a small case study for Latin is available in Baayen, Chuang and Blevins (2018, The Mental Lexicon).  I am both excited and puzzled that the simple linear mappings underlying Linear Discriminative Learning works so well.


In my lab, we are using electromagnetic articulography and ultrasound to clarify how speakers move their jaw and tongue during articulation.  We have been studying dialect differences (speakers in the north-east of the Netherlands speak with their tongue further back in the mouth compared to speakers in the center east, Wieling et al. 2016, Journal of Phonetics), and we have recently obtained evidence that practice makes perfect also for articulation (Tomaschek et al., 2018, Linguistic Vanguard).  We are also modeling the different acoustic durations of homophonous suffixes (e.g., English -s, which on nouns expresses plural or genitive, and on verbs the third person singular) using discriminative learning.

Statistical methods.

I have a long-standing interest in statistical methods, including linear mixed effects models, random forests, generalized additive models, quantile regression, and survival analysis.  I am especially impressed by the
combination of quantile regression and generalized additive modeling as implemented in the qgam package for R by Matteo Fasiolo (University of Bristol).  I love exploratory data analysis and have learned most from those experiments that flatly contradicted my predictions, and revealed unexpected new trends in my data.



WS 2021/2022
Linguistics for Cognitive Science

SS 2021
Methods II: Statistics

WS 2020/2021
Linguistics for Cognitive Science

WS 2019/2020
Linguistics for Cognitive Science

SS 2019
Computational Models of Morphological Processing

SS 2019
Introduction to Regression and Data Analysis

WS 2018/2019
Introduction to Linguistics for Cognitive Science

SS 2018
Advanced Regression Modeling

SS 2018
Introduction to Regression and Data Analysis

SS 2018
Introduction to Linguistics for Cognitive Science

Selected publications

Shafaei-Bajestan, E., M. Moradipour-Tari, P. Uhrig, and R. H. Baayen (2021). LDL-AURIS: A computational model, grounded in error-driven learning, for the comprehension of single spoken words. Language, Cognition and Neuroscience, 1-28. Pdf

Shen, T., and R. H. Baayen. (2021). Adjective-Noun Compounds in Mandarin: a Study on Productivity. Corpus Linguistics and Linguistic Theory, 1-30. Pdf

Tomaschek, F., Tucker, B.V., Ramscar, M., and Baayen, R. H. (2021). Paradigmatic enhancement of stem vowels in regular English inflected verb forms. Morphology, 31, 171-199. Pdf

Chuang, Y-Y., Vollmer, M-l., Shafaei-Bajestan, E., Gahl, S., Hendrix, P., and Baayen, R. H. (2020). The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using Linear Discriminative Learning. Behavior Research Methods, 1-51. pdf

Baayen R.H., Chuang Y., Shafaei-Bajestan E., Blevins J.P. (2019). The discriminative lexicon: A unified computational model for the lexicon and lexical processing in comprehension and production grounded not in (de)composition but in linear discriminative learning. Complexity, 1-39. pdf

Sering K., Milin P., Baayen R. H., (2018). Language comprehension as a multi-label classification problem. Statistica Neerlandica, 72, 339–353. pdf

Tomaschek F., Tucker B.V., Fasiolo M., and Baayen R.H. (2018). Practice makes perfect: The consequences of lexical proficiency for articulation. Linguistics Vanguard, 4, 1-13. pdf

Baayen R.H., Vasishth S., Kliegl R., Bates D. (2017). The cave of shadows. Addressing the human factor with generalized additive mixed models. Journal of Memory and Language, 94:206-234. pdf

Linke M., Bröker F., Ramscar M., Baayen R.H. (2017). Are baboons learning "orthographic" representations? Probably not. PLoS ONE 12(8): e0183876. pdf

Baayen R.H., Shaoul C., Willits J., Ramscar M. (2015). Comprehension without segmentation: A proof of concept with naive discriminative learning. Language, Cognition, and Neuroscience, 31:106–128. pdf

Ramscar M., Baayen R.H. (2014). The myth of cognitive decline: why our minds improve as we age. New Scientist, 221(2961):28–29. pdf

Baayen R.H., Milin P., Durdević D. F., Hendrix P., Marelli M. (2011). An amorphous model for morphological processing in visual comprehension based on naive discriminative learning. Psychological Review, 118:438–482. pdf

Baayen R.H. (2008). Analyzing linguistic data: A practical introduction to statics using R. Cambridge University Press. pdf

Baayen, R.H. (2001). Word Frequency Distributions, Kluwer Academic Publishers. pdf

Presentations in the last 2 years


Baayen, R. H., and Chuang, Y.-Y., Modeling morphology with multivariate multiple regression, Workshop Recent Approaches to the Quantitative Study of Language: Rules and Un-rules, Neuchatel, Switzerland, October 14, 2021 (virtual presentation).

Shen, T., and Baayen, R. H., Productivity and semantic transparency: An exploration of compounding in Mandarin, Workshop Perspectives on productivity, Leuven, Belgium, May 26, 2021 (virtual presentation)

Baayen, R. H., and Gahl, S., Thyme and time again: Semantics all the way down, Internal Workshop FOR2373, Düsseldorf, Germany, March 18, 2021 (virtual presentation).

Luo, X., Chuang, Y-Y., and Baayen, R. H., Linear Discriminative Learning in Julia, International Conference on Error-Driven Learning in Language (EDLL 2021), Tübingen, Germany, March 11, 2021 (virtual poster presentation).


Sering, K., Schmidt-Barbo, P., Otte, S., Butz, M. V., and Harald Baayen, R. H., Recurrent Gradient-based Motor Inference for Speech Resynthesis with a Vocal Tract Simulator, the 12th International Seminar on Speech Production (ISSP 2020), New Haven, USA, December 14, 2020 (virtual poster presentation).

Baayen, R. H., A multivariate multiple regression approach to the mental lexicon, the 28th International Conference on Computational Linguistics (COLING’2020), Barcelona, Spain, December 8, 2020 (virtual invited talk).

Baayen, R. H., Quantitative Cognitive Linguistics, Hindustan Institute of Technology & Science, Chennai, India, November 23, 2020 (virtual talk)

Baayen, R. H., A discriminative perspective on learning a new language, 7th International Scientific Interdisciplinary Conference on Research and Methodology, Moscow, Russia, November 20, 2020 (virtual keynote).

Baayen, R. H., and Chuang, Y.-Y., How long you make your words crucially depends on their meanings, PACLIC 2020 - The 34th Pacific Asia Conference on Language, Information and Computation, Hanoi, Vietnam, October 24, 2020 (virtual talk).

Li, J., Chuang, Y.-Y., and Baayen, R. H., Tonal (ir)regularity and word frequency in Mandarin bisyllabic compounds, Words in the World International Conference 2020, St. Catharines, Canada, October 18, 2020 (virtual talk).

Saito, M., Tomaschek, F., and Baayen, R. H., Co-articulation between stem vowels and suffixes: semantics all the way down, Words in the World International Conference 2020, St. Catharines, Canada, October 18, 2020 (virtual talk).

Luo, X., Chuang, Y.-Y., and Baayen, R. H., Implementation of Linear Discriminative Learning in Julia, Words in the World International Conference 2020, St.Catharines, Canada, October 16, 2020 (virtual talk).

Baayen, R. H., Chuang, Y.-Y., and Shafaei-Bajestan, E., Using discriminative learning to model comprehension and production of inflectional morphology without morphemes and without inflectional classes, Workshop How to fill a cell: computational approaches to inflectional morphology, Sheffield, United Kingdom, September 16, 2020 (virtual talk).

Baayen, R. H., A blueprint for discriminative learning of simple utterances, TÜling Linguistics Lectures, Tartu, Estonia, May 5, 2020 (virtual talk).

Chuang, Y.-Y., Lõo, K., Blevins, J. P., and Baayen, R. H., Estonian case inflection made simple. A case study in word and paradigm morphology with linear discriminative learning, IMM19/PsyComMT, Vienna, Austria, February 7, 2020.

Heitmeier, M., and Baayen, R. H., Simulating phonological and semantic impairment of English tense inflection with linear discriminative Learning, IMM19/PsyComMT, Vienna, Austria, February 7, 2020.

Shafaei-Bajestan, E., and Baayen, R. H., Wide learning of the comprehension of morphologically complex words: from audio signal to semantics, IMM19/PsyComMT, Vienna, Austria, February 7, 2020.


Baayen, R. H., A dynamic approach to lexical processing, The 1st NTü Linguistic Workshop, Taipei, Taiwan, November 28, 2019 (keynote).

Baayen, R. H., Construction morphology, linear discriminative learning, and cognitive reality, workshop The Constructionist Challenge – empirical and theoretical aspects, Erlangen, Germany, October 18, 2019 (invited).

Baayen, R. H., Wide learning in language modeling, Colloquium ICCLS - Interdisciplinary Centre for cognitive Language Studies, München, Germany, June 17, 2019 (invited).

Baayen, R. H., Throwing off the shackles of the morpheme with simple linear transformations, Colloquium for Computational Linguistics and Linguistics in Stuttgart, Stuttgart, Germany, April 29, 2019 (invited).

Baayen, R. H., Wide learning in language modeling, Vienna University of Economics and Business, Vienna, Austria, March 15, 2019 (invited).

Chuang, Y. Y., Baayen, R. H., Making sense of auditory nonwords, Workshop - "Models of Computational Morpho(phono)logy", Cambridge, UK, February 15, 2019 (invited).

Baayen, R. H., Linear discriminative learning and the bilingual lexicon, A Language Learning Roundtable, Fribourg, Switzerland, February 11, 2019 (invited).