Learning Speaker Recognition Models through Human Human-Robot Interaction

摘要：

Person identification is the problem of identifying an individual that a computer system is seeing, hearing, etc. Typically this is accomplished using mo models of the individual. Dels Over time, however, people change. Unless the models stored by the robot change with them, those models will became less and less reliable over time. This work explores automatic updating of person identification models in the domai domain of speaker n recognition. By fusing together tracking and recognition systems from both visual and auditory perceptual modalities, the robot can robustly identify people during continuous interactions and update its models in real real-time, improving rates of speaker classification.

作者: E. Martinson W. Lawson

作者单位: U.S. Naval Research Laboratory,Washington,DC 20375 U.S. Naval Research Laboratory,Washington DC

会议类型: 国际会议

会议名称: 2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)

会议地点: 上海

会议语种:英文

页码: 3915-3920

在线出版日期: 2011-05-09（万方平台首次上网日期，不代表论文的发表时间）