Feasibility of Big Data Analytics to Assess Personality Based on Voice Analysis

(1) Background: As far back as the 1930s, it was already thought that gestures, clothing, speech, posture, and gait could express an individual's personality. Different research programs, some focused on linguistic cues, were launched, though results were inconsistent. The development of new speech analysis technology and the generalization of big data analysis have created an opportunity to test the predictive power of voice features on personality dimensions. This study aims to explore the feasibility of an automatic personality assessment system in the context of personnel selection. (2) Methods: One hundred participants were recorded during an individual interview for voice analysis. They also completed the NEO-FFI and were required to ask and collect the assessment of their personality by a close significant other. Furthermore, an expert estimated participants' personality dimensions based on the viewing of the recorded interviews. (3) Results: Results showed there are specific voice features related to the externalization of individuals' personalities (predictions ranging from 0.3 to 0.4). Voice features also predicted significant others' estimations and expert ratings of the target individual's personality, though the features were not exactly the same. (4) Conclusions: It is noteworthy that predictions were made based on voice recordings obtained using ordinary devices in controlled but not restricted speech situations, which may make such an approach a promising tool for personality assessment in contexts such as personnel selection.