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 in the data. This leads to two
modeling approaches. In the first approach, the dynamics of the feature set within the …