Dialog Act Classification from Prosodic Features Using Support Vector Machines

In this work we investigate the use of support vector machines (SVMs) and discriminative learning techniques on the task of automatic classification of dialogue acts (DAs) from prosodic cues. We implement and test these classifiers on solving an 8DA classification task on the Spanish CallHome database and report preliminary recognition rates of 47.3% with respect to a 20.4% chance-level rate, which represents an improvement over previously reported work using decision trees and neural network classifiers. Although prosodic cues alone may not suffice for robust classification of DAs, we report results that suggest that SVMs offer an interesting alternative to previously explored models, and should be further explored to improve the contribution of prosodic models to the classification task.