Towards the use of inferred cognitive states in language modeling

Abstract

In spoken dialog, speakers are simultaneously engaged in various mental processes, and it seems likely that the word that will be said next depends, to some extent, on the states of these mental processes. Further, these states can be inferred, to some extent, from properties of the speaker's voice as they change from moment to moment. As a illustration of how to apply these ideas in language modeling, we examine volume and speaking rate as predictors of the upcoming word. Combining the information which these provide with a trigram model gave a 2.6% improvement in perplexity.