Interpreting symptoms of cognitive load in speech input

Autor: Berthold, Andr'e and Jameson, Anthony

Herausgeber:

Users of computing devices are increasingly likely to be subject to situationally determined distractions that produce exceptionally high cognitive load. The question arises of how a system can automatically interpret symptoms of such cognitive load in the user's behavior. This paper examines this question with respect to systems that process speech input. First, we synthesize results of previous experimental studies of the ways in which a speaker's cognitive load is reflected in features of speech. Then we present a conceptualization of these relationships in terms of Bayesian networks. For two examples of such symptoms--sentence fragments and articulation rate--we present results concerning the distribution of the symptoms in realistic assistance dialogs. Finally, using artificial data generated in accordance with the preceding analyses, we examine the ability of a Bayesian network to assess a user's cognitive load on the basis of limited observations involving these two symptoms.