In this paper we explore the use of features derived from multiresolution analysis of speech and the Teager Energy Operator for classification of drivers' speech under stressed conditions. We apply this set of features to a database of short speech utterances to create user-dependent discriminants of four stress categories. In addition we address the problem of choosing a suitable temporal scale for representing categorical differences of the data. This leads to two sets of modeling techniques. In the first approach, we model the dynamics of the feature set within the utterance with a family of dynamic classifiers. In the second approach, we model the mean value of the features across the utterance with a family of static classifiers. We report and compare classification performances on the sparser and full dynamic representations for a set of four subjects. Compressed Postscript . PDF . Full list of tech reports