Especially with mobile systems, one important part of the context of use involves psychological variables like cognitive load and time pressure. This abstract looks at one possible way of capturing such aspects of context: the analysis of features of the users’ speech. In a replication and extension of an earlier study of our group, we created four experimental conditions that varied in terms of whether the user was (a) navigating within a simulated airport terminal or standing still; and (b) subject to time pressure or not. The speech produced by these subjects was coded in terms of 7 variables. We trained dynamic Bayesian networks on the resulting data in order to see how well the information in the users’ speech could serve as evidence as to which condition the user had been in. The results give information about the accuracy that can be attained in this way, the methods that can be used to implement the classifiers, and the diagnostic value of some specific features of speech. 1 Background and Motivation When we think about modeling and representing context, we smay think first in terms of sensors that directly detect features of the environment, such as the location of the user, the presence of other persons, physical features like temperature and noise level, or activities that the user is engaged in. But the importance of these features of context is often due to the psychological effects that they have on the user. For example, the fact that a user is engaged in communication with other persons may be important mainly because it implies that the user has little time and attention left over for interacting with a system. It is therefore natural to view the contextually influenced psychological states of the user as constituting an important part of the context. But how can these psychological states be detected by a system? The research summarized here was supported by the German Science Foundation (DFG) in its Collaborative Research Center on Resource-Adaptive Cognitive Processes, SFB 378, Projects B2 (READY) and A2 (VEVIAG). We thank one of the anonymous reviewers for perceptive comments on the submitted version of the manuscript.