Automatic personality recognition and perception using deep learning and supervised evaluation method

Journal of Applied Research on Industrial Engineering

article

Effat Jalaeian Zaferani¹  Mohammad Teshnehlab¹  Mansour Vali¹ 

[1] Department of Electrical and Computer Engineering, Faculty of K. N. Toosi University of Technology

关键词: Stacked Auto-encoder;    Cross Fold Validation;    Big Five Inventory;    personality traits;    Speech Processing;   

DOI  :  10.22105/jarie.2021.282961.1314

学科分类：外科医学

来源: Ayandegan Institute of Higher Education

【 摘 要 】

The personality in the present world plays a critical role in social interactions, the use of modern technologies, and individuals' success. Therefore, in the last two decades, the study of Automatic Personality Perception (APP) and Automatic Personality Recognition (APR) has become more prevalent than speech processing. These studies have shown that personality traits affect acoustic features. However, the intrinsic imbalanced distribution of personality classes across the dataset is an issue mentioned in most previous studies and the classification results suffer from it. In this paper, an innovative supervised k-fold Cross-Validation (CV) method was proposed to cope with the problem of affecting the imbalanced distribution of data across different classes. The classification outcomes showed better performance in comparison with three traditional data balancing methods. Moreover, the obtained results of the proposed evaluation method indicated that the proposed method acts as a k-fold CV method if the data distribution is balanced; otherwise, it will improve the classification results.

【 授权许可】

CC BY