Gabriel J. Cler is a postdoctoral fellow at in the Speech & Brain Research Group and Wellcome Centre for Integrative Neuroimaging at the University of Oxford. His research with Kate Watkins focuses on neuroimaging in people with speech and language disorders. In addition to leveraging large existing datasets, he works in concert with ongoing projects in developmental language disorder (BOLD) and stuttering (INSTEP). His work with Prof. Watkins is co-mentored by Prof. Steve Smith and funded by a fellowship with the United States National Institutes of Health - National Institute on Deafness and Other Communication Disorders (NIDCD).
Gabe completed his PhD in Computational Neuroscience at Boston University in 2018 with Cara Stepp and co-mentors Frank Guenther and Jay Bohland. He has a BS in Computer Science from Bradley University and a Graduate Certificate in Cognitive Science from the University of Central Florida. Gabe’s research involves applying quantitative and computational techniques to rehabilitate speech motor control disorders in children and adults. His work in the Stepp Lab for Sensorimotor Rehabilitation Engineering focused on developing and validating novel communication interfaces for individuals with severe paralysis, developing quantitative assessment and videogame rehabilitation for individuals with resonance disorders, and applying multivariate analysis methods to kinematic speech data.
Optimized and Predictive Phonemic Interfaces for Augmentative and Alternative Communication.
Cler GJ. et al, (2019), J Speech Lang Hear Res, 62, 2065 - 2081
The Effects of Modulating Fundamental Frequency and Speech Rate on the Intelligibility, Communication Efficiency, and Perceived Naturalness of Synthetic Speech.
Vojtech JM. et al, (2019), Am J Speech Lang Pathol, 28, 875 - 886
Transmasculine Voice Modification: A Case Study.
Buckley DP. et al, (2019), J Voice
Longitudinal Case Study of Transgender Voice Changes Under Testosterone Hormone Therapy.
Cler GJ. et al, (2019), J Voice
Prediction of Optimal Facial Electromyographic Sensor Configurations for Human-Machine Interface Control.
Vojtech JM. et al, (2018), IEEE Trans Neural Syst Rehabil Eng, 26, 1566 - 1576