Analysis of Temporal Features for Interaction Quality Estimation
This work addresses improving IQ estimation for Spoken Dialogue Systems, but it is incremental as it focuses on feature analysis and optimization.
The paper tackled the problem of estimating Interaction Quality (IQ) in Spoken Dialogue Systems by analyzing temporal features, finding that these features contribute most to classification performance and that modifying window size increased overall performance by +15.69%.
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%.