Alexandra Mayn


Familiar words but strange voices: Modelling the influence of speech variability on word recognition
Alexandra Mayn | Badr M. Abdullah | Dietrich Klakow
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop

We present a deep neural model of spoken word recognition which is trained to retrieve the meaning of a word (in the form of a word embedding) given its spoken form, a task which resembles that faced by a human listener. Furthermore, we investigate the influence of variability in speech signals on the model’s performance. To this end, we conduct of set of controlled experiments using word-aligned read speech data in German. Our experiments show that (1) the model is more sensitive to dialectical variation than gender variation, and (2) recognition performance of word cognates from related languages reflect the degree of relatedness between languages in our study. Our work highlights the feasibility of modeling human speech perception using deep neural networks.


Towards Generating Effective Explanations of Logical Formulas: Challenges and Strategies
Alexandra Mayn | Kees van Deemter
2nd Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence

While the problem of natural language generation from logical formulas has a long tradition, thus far little attention has been paid to ensuring that the generated explanations are optimally effective for the user. We discuss issues related to deciding what such output should look like and strategies for addressing those issues. We stress the importance of informing generation of NL explanations of logical formulas through reader studies and findings on the comprehension of logic from Pragmatics and Cognitive Science. We then illustrate the discussed issues and potential ways of addressing them using a simple demo system’s output generated from a propositional logic formula.