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Paper WeP201.3

Martinson, Eric (US Naval Research Laboratory), Lawson, Wallace (US Naval Research Laboratory)

Learning Speaker Recognition Models through Human-Robot Interaction

Scheduled for presentation during the Regular Sessions "Cognitive Human-Robot Interaction" (WeP201), Wednesday, May 11, 2011, 15:55−16:10, Room 3B

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on December 8, 2019

Keywords Human detection & tracking, Recognition, Cognitive Human-Robot Interaction

Abstract

Person identification is the problem of identifying an individual that a computer system is seeing, hearing, etc. Typically this is accomplished using models of the individual. 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 domain of speaker 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-time, improving rates of speaker classification.

 

 

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