Smart meeting room: ubiquitous speech recognition and analysis of mental states of attendees in meetings**

低效的会议每年使组织损失数十亿美元。造成这个问题的一个重要因素是缺乏对会议上发生的对话的准确了解，以及对与会者的认知和情绪状态的客观分析。对与会者的认知和情绪状态进行准确的记录和分析，将使管理者和决策者能够客观地监控和优化会议的持续时间、频率和结构。因此，该项目的目标是开发一种强大的方法来监控会议中的与会者，以便记录他们的对话并量化/分类他们的认知负荷和情绪。此外，准确的会议记录将允许自动生成会议记录和任务分配/管理。该项目将培养2名博士生、1名硕士生、2名本科生，首先配置一个能够录制音频、视频和压力数据的智能会议室。将开发新的语音源分离方法，以解决同步对话和实现特定说话风格的个性化。个性化和自适应的语音识别方法将被开发用于语音和会话的转录。为了对认知和情绪状态进行分类和量化，将开发出检测心率（通过带压力传感椅的ballocardiography）和呼吸速率的稳健方法。最后，将开发新的学习模型，能够准确地将这些模式以及衍生特征映射到情绪和认知状态。该项目将通过开发语音识别和认知/情感计算领域的新算法和智能系统，为加拿大的人工智能生态系统做出贡献。此外，该项目可以通过优化会议为加拿大企业做出重大贡献，从而有可能带来财务增长并吸引顶尖人才。该项目得到了IMRSV数据实验室的支持，这是一家位于渥太华的加拿大公司，活跃于人工智能和机器学习。****

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 2 PhD students, 1 master's student, and 2 undergraduate students, and begin by first configuring an intelligent meeting room capable of recording audio, video, 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) and respiration rate, 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 AI ecosystem 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, 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.****