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.
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