Together with Ulrike Domahs and Frank Domahs, I have developed a classifier that predicts what syllable should be stressed in a German word or a non-word with German orthography, when it is read out loud.

Thanks to the initiative of Frank Domahs, I now have developed an online application which researchers can use to make predictions for written words which they are going to use in experiments. You can find the app here or under “Ressources” where you may find other useful things for your research and studies.

If you happen to be interested in running non-linear statistical analysis, I have written a tutorial for running Generalized Additive Models in R. This type of model allows you to fit a dependent variable with a numeric predictor and shows you where the non-linear relationships are. You can find the tutorial either in my Ressources, or jump directly to my osf repository where you can download both, the tutorial and the necessary data.

I am very happy to announce that Michael Ramscar and I got a paper published in Frontiers in Psychology. The topics are a) cue-outcome structure in modelling and b) phonetic characteristics of German “a-schwa” in different morphological functions. We have found that a phones morphological functions — in our case those of aschwa — and should be treated as inputs to a computational model of speech cognition, because they also serve as cognitive cues during speech production.

More over, we investigated a-schwa’s phonetic characteristics. We have found that these differ depending on the word’s class that the a-schwa is in. For example, the a-schwa at the end of the content word “Lehrer” has systematically different characteristics than that in the conjunction “aber”.
Crucially, we have found that the degree of experience between these morphological functions and the gesture of that phone — which speakers accumulate over their life time — modulates a-schwa’s phonetic characteristic.

This link leads you to the open source version of the paper.

I implemented the pi-gesture theory by Dani Byrd and Elliot Saltzman (2003) in R code which proposes the “pi-gesture” to modulate gestural coordination. You can access the simulation on this website. You can modulate different aspects of articulation such as the strength of the pi-gesture, the duration of the gesture, etc..

I am so happy to announce that we have successfully published a paper on the articulation of stem vowels in morphologically complex words in Morphology (“Paradigmatic enhancement of stem vowels in regular English inflected verb forms”). It has been a long journey. I wrote the first draft of this paper four years and three months ago. The data was recorded in 2013!

In our study, that I performed together with Michael Ramscar, Benjamin Tucker and Harald Baayen, we find that articulatory movements are partially more extreme in high-frequency words than in low-frequency words, supporting our previous findings in articulation and simultaneously contradicting predictions by the Smooth Signal Redundancy Hypothesis.

In our analysis, we used Generalized Additive Mixed Models, a regression technique that allows to investigate non-linear relationships between a dependent variable and one or multiple predictors. If you want to investigate such relationships on your own, the paper provides you with a good introduction to how assess the relative importance of potentially competing and collinear predictors. The supplementary material also provides you with the necessary data and code.

If you want to learn what these findings tell us about speech production, you can find the paper here. If you want to know more about speech production and articulation, check out my other publications here.

This is a nice jewel I just found on YouTube which I need to share here.

Larry McEnerney, Director of the University of Chicago’s Writing Program, talks about what is important in a scientific text. He discusses how to properly formulate a problem for the community of readers a paper is aiming at. Very insightful.

A while ago I gave some tips and tricks how to start a scientific manuscript (click here). The idea was to record yourself while you are talking about your research, transcribe the recording and then edit the transcription.

I have not tackled, however, how to actually write and structure your text, build an argument, tell a story. And you know why? Because it is such a complicated process that I do not feel qualified enough to talk about it. At least not yet.

Yes, a scientific text is a story. Scientists are writers and well written papers are key to letting the world knowing about your world. Thanks to a colleague of mine, I discovered the teaching of Judy Swan – Associate Director for Writing in Science and Engineering. One key stone of her career is to communicate about scientific writing. Her key insight is that texts do not have a fixed interpretation. “Words do not have meanings, they have interpretations”, she states. The interpretation of words, sentences, entire texts depends on the contexts. Therefore, once a texts gets published, interpretations depends on the readers and how much they know and how much they understand what we write.

Good scientific writing manages the readers’ expectations. It begins at the level of the paper’s structure – introduction, methods, results, discussion – but it carries on at the level of paragraphs and sentences sentences. In a well written scientific text, important pieces of information are placed in locations where readers expect emphasis on important pieces of information.

Enjoy the video.

Yes, is the short answer. The long answer: it depends on how well you learned the connection between a morphological function and  the word, the sound and the context in which the word is located.

Long story short: the better you learned this connection, and consequently the more sure you are the message you want to convey, the longer you make the phonetic signal.

Together with my colleagues Mirjam Ernestus, Ingo Plag and Harald Baayen we were able to show this relation in word-final [s] or [z] in American English and the morphological function it expresses, e.g. plural in ‘dogs’ or third person singular in ‘takes’.

To simulate learning with a computer, we used Naive Discriminative Learning (NDL), a computational formalization of Discriminative Learning. Discriminative learning assumes that speakers learn to discriminate their environment by language. Listeners on the other hand learn to use the perceived signal to predict the message the speaker intended. The theory predicts that the amount of experience speakers and listeners have with the relation between message and signal will affect their behavior. In speakers, it is the behavior is the production of the speech signal which encodes the message; in listeners, the behavior is how they respond to the signal. Here, we studied the speakers.

We operationalized the intended message as the morphological function a word has to convey and the cues as the intended acoustic signal. NDL thus learned to discriminate the morphological function on the basis of the acoustic cues. Crucially, our model used cues from the target word such as ‘dogs’ or ‘takes’, but also from words in the context of those words. The reason for this is that we assume that a word’s morphological function is not only encoded by the acoustics of the word itself, but by the acoustics of the words surrounding it.

NDL further allows to calculate measures about 1) how well the morphological function is supported by a set of cues and 2) how uncertain the model is about the lexical information. Take a driving on the highway as an example. Let’s say, you are waiting for an exit to leave the highway and finally a sign occurs. The larger an exit sign on the street, the more support you have that an exit will occur. However, the size of the sign does not say anything about how certain (or uncertain) you actually are that it is actually the exit you should take. Both will affect how you will behave on the street.

To sum it up, NDL allows you to simulate how morphological function and the phonetic signal are connected and therefore to investigate how the processes guiding speech production affect the phonetic signal

The entire study will be published under the title:

Phonetic effects of morphology and context: Modeling the duration of word-final S in English with NaÏve Discriminative Learning

It has been accepted for publication by the “Journal of Linguistics” and is being prepared for publication. A pre-publication version can be downloaded from psyarxive here.

The paper also provides a very good introduction to Discriminative Learning, how it is performed and how it can be used for predicting phonetic characteristics. If you want to perform an NDL learning simulation on your own, you can find an introduction to this technique in my R introduction here.