Faculty member Ivano Caponigro has edited the volume Headless Relative Clauses in Mesoamerican languages (Oxford University Press) together with Prof. Harold Torrence (UCLA) and Prof. Roberto Zavala Maldonado (CIESAS, Mexico). The book results from a 4-year project in which a team of 21 scholars from Mexico, USA, Canada, and France investigated the morpho-syntax and semantics of several varieties of headless relative clauses across 15 languages (all from Mesoamerica but one) by adopting the same template, definitions, and data collection methodologies.
Link to the Publisher’s book webpage (with book endorsements)
Link to the project website (with video interviews to each scholar involved in the project)
Link to the book webpage within the project website (with freely available chapters)

Faculty member Eva Wittenberg, PI of the Language Comprehension Lab, just received both an IBM University Award and a UCSD Innovation Grant for Inclusive Research Excellence, to better understand how the COVID-19 pandemic influences language comprehension throughout society, in an exciting ongoing project with colleagues Rachel Ostrand, Dan Kleinman, and Adam Morgan. Dr. Wittenberg was also awarded a Yankelovich Center Book Manuscript/Grant Proposal Improvement Grant.

Faculty member Ivano Caponigro just a published a paper entitled “Unveiling multiple wh– free relative clauses and their functional wh-words” on Semantics & Pragmatics together with Dr. Anamaria Fălăuş.

Faculty member Eva Wittenberg has been awarded a UC San Diego Division of Social Sciences research grant to investigate counterfactuals like, “If I had bought toilet paper in February, I would have one fewer worry right now.” Congratulations, Eva!

Faculty member Ivano Caponigro is an invited speaker at the workshop on “Non-interrogative subordinate wh-clauses” at the University of Cologne (Germany) on February 7-8, 2020.

Faculty member Will Styler was invited to give a talk at UCLA’s Department of Linguistics on November 15th.

Title: Using Transparent Machine Learning to study Human Speech

Abstract:

Machine learning, the use of nuanced computer models to analyze and predict data, has a long history in speech recognition and natural language processing, but have largely been limited to more applied, engineering tasks. This talk will describe two more research-focused applications of transparent machine learning algorithms in the study of speech perception and production.

For speech perception, we’ll examine the difficult problem of identifying acoustic cues to a complex phonetic contrast, in this case, vowel nasality. Here, by training machine learning algorithms on acoustic measurements, we can more directly measure the informativeness of the various acoustic features to the contrast. This by-feature informativeness data was then used to create hypotheses about human cue usage, and then, to model the observed human patterns of perception, showing that these models were able to predict not only the utilized cue, but the subtle patterns of perception arising from less informative changes.

For speech production, we’ll focus on data from Electromagnetic Articulography (EMA), which provides position data for the articulators with high temporal and spatial resolution, and discuss our ongoing efforts to identify and characterize pause postures (specific vocal tract configurations at prosodic boundaries, c.f. Katsika et al. 2014) in the speech of 7 speakers of American English. Here, the lip aperture trajectories of 800+ individual pauses were gold-standard annotated by a member of the research team, and then subjected to principal component analysis. These analyses were then used to train a support vector machine (SVM) classifier, which achieved a 96% classification accuracy in cross-validation tests, with a Cohen’s Kappa showing machine-to-annotator agreement of 0.79, suggesting the potential for improvements in speed, consistency, and objective characterization of gestures.

These methods of modeling feature importance and classifying curves using transparent and interpretable machine learning both demonstrate concrete methods which are potentially useful and applicable to a variety of questions in phonetics, and potentially, in linguistics in general.

Faculty member Michelle Yuan just published a new paper on “Dependent case and clitic dissimilation in Yimas” in Natural Language and Linguistic Theory.