An Exploration of Automatic Personality Classification Using Different Speech Styles

Abstract

The field of speech-based Personality Computing classifies personality traits using speech data. There are two labelling methods for this: Automatic Personality Recognition (APR), using self-assessed personality scores, and Automatic Personality Perception (APP), using externally rated personality scores. Another aspect is whether the data is recorded in natural circumstances or in a controlled environment, as this influences how personality is shown. There is a lack of research into these speech styles, especially when combined with the labelling methods. Related fields have been found to be more developed in two ways. First, research from the perspective of speech styles has already been conducted and proven useful. Second, when state-of-the-art techniques are released, such as pretrained models targetting speech, these fields are often included in benchmark tests. As no personality datasets are included, this creates a knowledge gab on using these techniques for personality classification.

The influence of the labelling methods and speech styles is investigated using three datasets that target APR with controlled and natural speech, and APP with natural speech.
Three types of models are used to see what personality traits can successfully be classified. The two APR datasets have not been used for personality classification before. Additionally, the models are trained using both hand-crafted features and embeddings extracted from a state-of-the-art pretrained model. The experiment on the APP and natural speech dataset indicates that the performance for 3 out of 5 traits can be improved using more effective features. The APR and controlled speech dataset was able to classify 4 out of 5 traits above chance. The APR and natural speech dataset could not well be classified. Overall, the APR datasets performed worse than the APP dataset. There were no clear patterns found between the speech styles. Furthermore, the embeddings showed better overall performance than the hand-crafted features. Future work could standardize a dataset for both labelling methods and speech styles to make direct comparison between the methods possible.