BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20251017T042739EDT-7399tfA1ou@132.216.98.100 DTSTAMP:20251017T082739Z DESCRIPTION:Unpacking the Speech Chain: A window of scientific and technolo gical opportunities\n\nSatrajit Ghosh\, MIT\n Tuesday October 18\, 12-1pm\n Zoom Link: https://mcgill.zoom.us/j/86855481591\n\nAbstract: Speaking is o ne of the most complex tasks carried out by humans and we do it\, mostly\, effortlessly. We use it to communicate our thoughts\, feelings\, and inte nt. In our group we have targeted this model system as a window into the h uman brain to understand basic brain and behavioral mechanisms\, and as a rich signal for developing new technologies and applications. Lately\, the re has been a significant increase in the attention to and relevance of th is seemingly simple timeseries thanks in large part due to pervasive growt h of smartphones\, voice assistants\, and machine learning technology. In this talk\, I will present the connectedness and current knowledge of spok en communication in relation to ongoing projects on stuttering\, on identi ty\, and on mental health. Each of these projects started with a simple qu estion that drew us in through different angles to understand the system b etter. Mumble melody\, the project on stuttering explores mechanisms to in duce greater fluency in people who stutter. A question of quantifying how similar two people sound led us to a project on identity and privacy. Evid ence of speech signals related to neuropsychiatry and neurology has spurre d intersecting speech and linguistics into mental health and wellbeing. Ea ch of these projects have made us question existing knowledge and models a nd has opened doors for new science\, technology\, and education. I will e nd by discussing the goals of a new consortial project recently launched b y the US National Institutes of Heath to study and evaluate voice as a bio marker across health. This project will focus on generating a rich\, diver se\, standardized\, and ethically considered dataset that accelerates the development of reduced-bias machine learning algorithms and technologies t hat can be used scalably\, reliably\, and remotely\, and address individua ls across social determinants of health.\n DTSTART:20221018T160000Z DTEND:20221018T170000Z SUMMARY:QLS Seminar Series - Satrajit Ghosh URL:/qls/channels/event/qls-seminar-series-satrajit-gh osh-342583 END:VEVENT END:VCALENDAR