Abstract

We describe a way of narrowing the search space
for descriptive keywords during a human-robot tutoring scenario,
where the tutor is explaining names and characteristics of objects
to the robot, by employing interaction detection techniques. This
system detects attention getting behaviour which is derived from
mother-infant interactions and extracts the verbal information
during these specific time periods, segmenting it and building
up histograms to estimate word frequencies and thus word
importance. This method should allow us to create a system
that does not rely on a dictionary or normal speech recognition
to acquire novel word-object relations but only relies on the pure
interaction between the robot and a human tutor.