Gasper Begus (original) (raw)

My website has moved to www.gasperbegus.com

I am an Associate Professor at the Department of Linguistics at UC Berkeley, where I’m also affiliated with the Institute of Cognitive and Brain Sciences. Previously, I was an Assistant Professor at the University of Washington. Before that, I graduated with a Ph.D. from Harvard.

My research focuses on developing deep learning models for speech data and using well-understood dependencies in speech to interpret internal representations in deep neural networks. More specifically, I build models that learn representations of spoken words from raw audio inputs. I combine machine learning and statistical models with neuroimaging and behavioral experiments to better understand how neural networks learn internal representations in speech and how humans learn to speak. I have worked and published on sound systems of various language families such as Indo-European, Caucasian, and Austronesian languages.

In a recent set of papers (here and here), I propose that language acquisition can be modeled with Generative Adversarial Networks and propose a technique for exploring the relationship between learned representations and latent space in deep convolutional networks.

I direct the Berkeley Speech and Computation Lab. Feel free to contact me if you’re interested in getting involved with the lab.

You can follow me on Twitter for latest updates.