Personality prediction based on intonation stylization

This study’s aim is to predict speaker personality from intonation patterns in spoken dialogs. Intonation patterns were extracted by a parametric superpositional stylization approach that allows for pattern description on a parametric as well as on a categorical level. Based on features derived from these representations we trained support vector machines and fitted generalized linear regression models to predict speaker personality with respect to the four dimensions acting, extroversion, other-directedness, and sensitivity. The personality classification accuracies ranged from 79 to 91%.