Speech Sound Category Learning

Joint work with Naomi Feldman and Ellen Lau

Being able to understand and comprehend a language involves mapping a continuous, multidimensional acoustic signal to discrete abstract speech categories.  While knowledge of a native language system is acquired early in life, the complexity of this mapping poses a difficult learning problem, particularly for second language learners who struggle to acquire the speech sounds of a non-native language.  The apparent rigidness of the system does not allow for much plasticity in representations of the native language.  The goal of my research is to advance our understanding of the speech perception system by focusing on the situations where it is robust to change, where it shows plasticity, and the systems that enable this.  I study effective mechanisms for updating one's speech category system to accommodate for the speech sounds of a second language and what neural circuits are active during these different learning paradigms.  My approach involves combining evidence from neural work with computational modelling of human behaviour.

Related Work: 

Thorburn C., Lau E., & Feldman, N. H. (2021)

"A reinforcement learning approach to speech category acquisition"

Proceedings of the Boston University Conference on Language Development (BUCLD) [paper]

Thorburn C., Lau E., & Feldman N. H. (2020). 

"An evaluation framework for acoustic-phonetic models of speech processing."

Presented at the Mid-Atlantic Student Colloquium on Speech, Language and Learning (MASC-SLL) [abtract] [poster]

Continuous Processesing of Speech in Non-Native English Speakers

Joint work with Ellen Lau and Jonathan Simon

While listening to speech humans are constantly predicting upcoming information as several levels on the linguistic hierarchy.  It has been shown that native English speakers make predictions about upcoming phonemes using both a local context model which account for purely sublexical information, and - separately - a global context model which uses information and word and sentence levels.  We are currently investigating how these context models may differ in non-native English speakers, using continuous MEG recordings.

Related Work: 

Thorburn C., Karunathilake I.M., Dixon L., Zhang M., Lau E., and Simon J. (2022)

"Local and Global Context Models in non-native English speakers"

Poster at the Annual Meeting Of The Society for Neurobiology of Language (SNL) [poster] [abstract]

The Language Familiarity Effect In Infancy

Joint work with Thomas Schatz and Naomi Feldman

Human listeners are better at telling apart speakers of their native language than speakers of other languages, a phenomenon known as the language familiarity effect.  While most accounts of this effect in adults require abstract phonological knowledge or comprehension of the speech itself, the effect has also been observed in infants as young as 4.5 months of age who are unlikely to have such sophisticated knowledge.  Using algorithms from unsupervised machine learning and automatic speech recognition we built a model to demonstrate how children may show this effect without requiring any sophisticated linguistic knowledge.

Related Work: 

Thorburn,C., Feldman N. H., & Schatz T. (2019).

"A quantitative model of the language familiarity effect in infancy."

Presentation at the 13th Northeast Computational Phonology Workshop (NEC-Phon) [slides]

Thorburn,C., Feldman N. H., & Schatz T. (2019).

"A quantitative model of the language familiarity effect in infancy."

Proceedings of the Conference on Cognitive Computational Neuroscience [poster]

Communicating Language Science to K-12 Students

Joint work with Kathleen Oppenheimer, Lauren Salig, Erika Exton, London Dixon and Alex Krauska

When the Covid-19 pandemic forced elementary and middle schools to move to online education, many forms of typical scientific outreach such as science fairs and school visits were no longer possible.  In the last two years, we have pivoted to virtual outreach activities - making innovative, fun long-form demonstrations that can be presented to classrooms over zoom.  We use green screens and a high degree of student interaction and distribute surveys to engage effectiveness.  Our research show students enjoy the activities and learn basic linguistic concepts and principles of the scientific method

More Information: @TheLanguageScientists Instagram, Website (coming soon!) 

Related Work: 

Oppenheimer K., Salig L.*, & Thorburn C.*, & Exton E.*  (2022)

"Taking Language Science to Zoom School: Virtual Outreach to Elementary School Students"

Language and Linguistics Compass [paper]

Oppenheimer K., Thorburn C.*, Salig L.*, Krauska A. *, Exton E.* & Dixon L.* (2022)

"Clinicians as STEMpowered Professionals: Using Speech and Language Science for Community and Career


To be presented at the Annual Meeting Of the American Speech-Language-Hearing Association (ASHA)

Exton E., Oppenheimer K., Sailig L., & Thorburn C. (2022). 

"Taking linguistics to Zoom school: Engaging children in virtual outreach"

Paper to be presented at the Annual Meeting of the Linguistic Society of America (LSA) [video]