Automatic Personality Estimation

This chapter describes how personality cues can be estimated from speech using an automated system. In order to enable a machine to come to a decision or estimation about any speakers’ personality a comprehensive processing chain needs to be developed. First, the recordings need to be preprocessed and segmented into meaningful chunks. Next, promising acoustic or prosodic cues need to be extracted from the signal. Because the realization of a feature candidate to be ‘promising’ can be expected to vary between the traits, this work incorporates a generic feature selection scheme in order to identify promising features from a completely data-driven perspective, namely the IGR ranking algorithm. Using promising features discriminative models are trained for classification and regression tasks using support vector machines. The whole learning scheme is evaluated by cross-validation applying an appropriate evaluation metric. Results are finally given by three data subsets, by classification and prediction success as well as by individual trait scores by means of confusion matrices, iterative accuracy plots and charts giving insights on feature space composition.