Linguistics Speaker Series: Naomi Feldman: Modeling Early Phonetic Learning
Naomi Feldman, University of Maryland
Modeling early phonetic learning from spontaneous speech
Most models of language acquisition have used idealized data. Phonetic category learning models, in particular, have been trained on inputs whose "acoustics" are artificially constructed to follow Gaussian distributions and abstract away from the time-varying nature of the speech signal. Furthermore, the acoustic dimensions used in phonetic learning models are typically hand-selected to be linguistically relevant.
This talk describes research that builds toward a theory of phonetic learning from naturalistic speech. The first half of the talk considers ways in which context might constrain phonetic category learning from speech that is highly variable, and finds that context can still be very helpful for learning from spontaneous speech -- but only when it is used in very specific ways. The second half of the talk raises the question of whether we should be thinking in terms of early phonetic category learning at all.
This is joint work with Kasia Hitczenko, Thomas Schatz, Stephanie Antetomaso, Emmanuel Dupoux, Micha Elsner, Sharon Goldwater, Reiko Mazuka, and Kouki Miyazawa.
Bio: Naomi Feldman is an associate professor in the Department of Linguistics and the Institute for Advanced Computer Studies at the University of Maryland, and a member of the Computational Linguistics and Information Processing Lab. She received her PhD in Cognitive Science from Brown University in 2011. She uses methods from machine learning to create formal models of how people learn and represent the structure of their language, and has been developing methods that take advantage of naturalistic speech corpora to study how listeners encode information from their linguistic environment.
Friday, March 15, 2019 at 3:30pm
Poulton Hall, 230
1421 37th St., N.W., Washington