I’m broadly interested in how the human brain perceives and processes information. Currently in my PhD, I study this through speech rhythm — temporal regularities and patterns of how syllables are organized. Some specific questions I am interested in exploring are how speech rhythm is represented in cognition, how it is learned by infants during the first few months of their lives, and how the learned representation of rhythm can interact when we learn a novel language of different rhythm structure.
Furthermore, temporal regularities in sequential information is not unique to speech. As such, I am also interested in how the brain parses other temporal regularities, and if speech differ from them. For example, in my first-year project, I explored how a model inspired by music rhythm processing can be applied to speech rhythm.
Before working on speech rhythm, I was also involved in a project of early phonetic learning, where machine learning models are employed to simulate the learning outcome of unsupervised learning of phonetic representations in infants. I tested the model under varying input conditions in a audiobook corpus I constructed, and found that models trained on unevenly distributed speaker — perhaps more similar to infants’ learning environments — do not generalize so well. This work was presented in CogSci 2020.
I also actively contributed towards building a speech corpus of Mandarin-accented English. The corpus contains speech of native American English and Mandarin-Accented English, with both isolated words and connected speech. A preliminary report of the corpus can be found here. I also learned to use (Bayesian) ideal adaptor models to model a listener’s adaptation to such accent using this corpus data.
Publications and Presentations (updated July, 2022)
2022
Li, R., Schatz, T., Feldman, N.H., 2022. Modeling rhythm in speech as in music: Towards a unified cognitive representation. To be presented at Cognitive Computational Neuroscience (CCN). [paper]
2020
Li, R., Schatz, T., Matusevych, Y., Goldwater, S., Feldman, N.H., 2020. Input matters in the modeling of early phonetic learning, in: Proceedings of the Annual Conference of the Cognitive Science Society. oral presentation, online. [paper]
2019
Li, R., Schatz, T., Matusevych, Y., Goldwater, S., Feldman, N.H., 2019. Modeling early phonetic learning: The effect of input size and speaker distribution, in: The Young Female Researchers in Speech Workshop. poster, Graz, Austria. [abstract | poster]
2018
Li, R., Xie, X., Jaeger, T.F., 2018. A corpus of native and non-native speech for speech production research, in: The 24th Annual Conference on Architectures and Mechanisms for Language Processing (AMLaP). poster, Berlin, Germany. [abstract | poster]
Xie, X., Li, R., Jaeger, T.F., 2018. Disentangling intra- and inter-talker variability in L2 phonetic production: L2 speech, but not talkers, is more variable, in: The 24th Annual Conference on Architectures and Mechanisms for Language Processing (AMLaP). poster, Berlin, Germany.
Xie, X., Li, R., & Jaeger, T. F., 2018. Perceiving native- and foreign-accented speech: A problem of probabilistic inference under uncertainty, in: International Max Planck Research School for the Language Sciences Conference (IMPRS). poster, Nijmegen, Netherlands.
Li, R, 2018. Speech variability across talkers and accidents, in: The National Conference of Undergraduate Research (NCUR). oral presentation, Oklahoma city, OK.