Gustav Henter

MSc students thesis projects available at this link: www.kth.se/profile/ghe/page/msc-thesis-proposals (The page is only visible if you are logged in to KTH.se, else the link gives error 404.)

I am an assistant professor in intelligent systems – specifically machine learning – at the Division of Speech, Music and Hearing (TMH). My primary research interests are probabilistic modelling for data generation, particularly 1) speech synthesis (text-to-speech) and 2) data-driven computer animation (body motion and co-speech gesticulation). Applications of the former include virtual assistants, prostethic voices, phonetics research, and assistive technologies for the visually impaired, and for the latter films, games, virtual avatars, social robots, and research on human-computer interaction. My work is supported by the national Wallenberg AI, Autonomous Systems and Software Program (WASP) initiative and partially also by the EACare project.

I completed my PhD at the Sound and Image Processing lab (SIP; later part of Communication Theory, which is now the Division of Information Science and Engineering) at the School of Electrical Engineering (EES) at KTH. During my PhD, I was a teaching assistant for the course EN2200/EN2202 Pattern Recognition, now EQ2341 Pattern Recognition and Machine Learning. I graduated in 2013 and moved on to a post-doc position at the Centre for Speech Technology Research (CSTR) at the University of Edinburgh, UK, followed by another post-doc in Prof. Junichi Yamagishi's research group at the National Institute of Informatics in Tokyo, Japan. I returned to KTH in April 2018, first as a post-doc and then, from January 2020, as an assistant professor.

For more information and resources, including full-text publications, presentations, etc., please see my professional homepage at KTH.