Research Area: Machine Learning
Identifying personality traits in children is a topic of interest due to its importance in adapting strategies for the teaching–learning process and detecting psychopathological features. The most straightforward procedure to identify childrens personality type is by applying a validated and suitable questionnaire according to their age. However, an interesting approach is automatically identifying the childrens personalities using their speech generated during their interaction with computer systems, software, or robots. This approach would allow obtaining the personality identification transparently to the children without answering a written test. This article presents a method for the automatic personality assessment of children between 8 and 12 years old. The assessment is based on their voices acoustic analysis while participating in playful activity with another child and a robot. We created a database with 98 children involved in several activities while their voices were recorded. The database was labeled with five paralinguistic aspects. Using these labels, we trained a set of classification models that helped us recognize primary and secondary personality traits according to the Childrens Personality Questionnaire. We obtained good results for two secondary personality traits, extroversion (0.89 F-Score) and excitability (0.79 F-Score). The best F-score obtained for anxiety was 0.70. These results indicate that it is feasible to estimate personality from analyzing childrens voices during interaction with computer systems.
Keywords:
Personality Assessment
Emotional Speech
Deep Learning
Machine Learning
Author(s) Name: Humberto Pérez-Espinosa,Benjamín Gutiérrez-Serafín,Juan Martínez-Miranda,Ismael E. Espinosa-Curiel
Journal name: Expert Systems with Applications
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.eswa.2021.115885
Volume Information: Volume 187, January 2022, 115885
