The main focus of my research is variation and variability — and how it is represented in the speakers’ and listeners’ minds. I investigate this from various perspectives. In the domains of speech production and speech perception I want to understand how language users learn to use variability, how it manifests itself in the speech signal and language use and how it can be predicted by social, idiosynchratic, lexical, and grammatical factor (e.g. inflection).
To do so, I employ different methods in my investigations. I employ Generalized Additive Mixed Effects Models (GAMS) or Linear Mixed Effects Regression (LMER) to conduct statistical analyses of information extracted from big acoustic data bases and recorded experimental data obtained with articulography. Rooted in the cognitive theory of Discriminative Learning, I also run computational learning simulations using the Naive Discriminative Learner (NDL) to understand the relationship between structure of knowledge representation and investigate what predictions these simulations make about linguistic behavior such as responses and phonetic signal. A thematically sorted presentations of my publications can be found here. A chronologically sorted list, including manuscripts in preparation and under revision is located here.
I advocate open-science and publish Supplementary Material to my studies on OSF. Links can be found in Publications. I have written an introduction to using the programming language R in which beginners can learn to program corpus analyses (and run NDL). This introduction can be found, together with more useful material, here.