BFI-based speaker personality perception using acoustic-prosodic features

This paper presents an approach to automatic prediction of the traits the listeners attribute to a speaker they never heard before. In previous research, the Big Five Inventory (BFI), one of the most widely used questionnaires, is adopted for personality assessment. Based on the BFI, in this study, an artificial neural network (ANN) is adopted to project the input speech segment to the BFI space based on acoustic-prosodic features. Personality trait is then predicted by estimating the BFI scores obtained from the ANN. For performance evaluation, the BFI with two versions (one is a complete questionnaire and the other is a simplified version) were adopted. The experiments were performed over a corpus of 535 speech samples assessed in terms of personality traits by experienced subjects. The results show that the proposed method for predicting the trait is efficient and effective and the prediction accuracy rate can achieve 70%.