  Many different approaches for estimating the Interaction Quality (IQ) of
Spoken Dialogue Systems have been investigated. While dialogues clearly have a
sequential nature, statistical classification approaches designed for
sequential problems do not seem to work better on automatic IQ estimation than
static approaches, i.e., regarding each turn as being independent of the
corresponding dialogue. Hence, we analyse this effect by investigating the
subset of temporal features used as input for statistical classification of IQ.
We extend the set of temporal features to contain the system and the user view.
We determine the contribution of each feature sub-group showing that temporal
features contribute most to the classification performance. Furthermore, for
the feature sub-group modeling the temporal effects with a window, we modify
the window size increasing the overall performance significantly by +15.69%.
