Learning Speaker Recognition Models through 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 2011
- Accession Number
- ADA550036
Entities
People
- E. Martinson
- W. Lawson
Organizations
- United States Naval Research Laboratory