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Mental States of Attendees in Meetings


Inefficient meetings cost organizations billions of dollars every year. An important factor that contributes to this problem is a lack of accurate knowledge regarding conversations taking place at meetings as well as an objective analysis of cognitive and emotional states of attendees. Accurate transcription of conversations and analysis of cognitive and emotional states of attendees will enable managers and decision-makers to monitor and optimize the duration, frequency, and structure of meetings objectively. Accordingly, the goal of this project is to develop robust methods for monitoring attendees in meetings in order to transcribe conversations and quantify/classify their cognitive load and emotions. Additionally, accurate transcription of meetings will allow for automated generation of minutes and task assignment/management. The project will train 1 PhD student, 1 master’s student, and 2 undergraduate students, and begin by first configuring an intelligent meeting room capable of recording audio, video, temperature, and pressure data. Novel methods for speech source-separation will be developed to address simultaneous conversations and enable personalization for particular speaking styles. Personalized and adaptive speech recognition methods will then be developed for transcription of speech and conversation. For classification and quantification of cognitive and emotional states, robust methods for detection of heart rate (through ballistocardiography with pressure sensing chairs), respiration rate, and temperature, will be developed. Finally, novel learned models, capable of accurately mapping these modalities along with the derived features to emotional and cognitive states will be developed. This project will contribute to Canada’s AIeco system through development of novel algorithms and intelligent systems in the fields of speech recognition and cognitive/affective computing. Additionally, the project can significantly contribute to Canadian businesses by optimizing meetings and increasing employee satisfaction, which in turn has the potential to result in financial growth and attract top talent. The project is backed by IMRSV Data Labs, a Canadian company based in Ottawa, active in AI and machine learning.


Sponsored by IMRSV Data Labs