Accession Number:

ADA550036

Title:

Learning Speaker Recognition Models through Human-Robot Interaction

Descriptive Note:

Corporate Author:

NAVAL RESEARCH LAB WASHINGTON DC

Personal Author(s):

Report Date:

2011-05-01

Pagination or Media Count:

7.0

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.

Subject Categories:

  • Anatomy and Physiology
  • Human Factors Engineering and Man Machine Systems

Distribution Statement:

APPROVED FOR PUBLIC RELEASE