Context Sensing using Speech and Common Sense
Nathan Eagle, Push Singh · 2004
We present a method of inferring aspects of a person’s context by capturing conversation topics and using prior knowledge of human behavior. This paper claims that topic-spotting performance can be improved by using a large database of common sense knowledge. We describe two systems we built to infer context from noisy transcriptions of spoken conversations using common sense, and detail some preliminary results. The GISTER system uses OMCSNet, a commonsense semantic network, to infer the most likely topics under discussion in a conversation stream. The OVERHEAR system is built on top of GISTER, and distinguishes between aspects of the conversation that refer to past, present, and future events by using LifeNet, a probabilistic graphical model of human behavior, to help infer the events that occurred in each of those three time periods. We conclude by discussing some of the future directions we may take this work.