1-D convolutional neural network based on the inner ear principle to automatically assess human’s emotional state

Abstract

The article proposes an original convolutional neural network (CNN) for solving the problem of the automatic voice-based assessment of a person’s emotional state. Key principles of such CNNs, and state-of-theart approaches to their design are described. A model of one-dimensional (1-D) CNN based on the human’s inner ear structure is presented. According to the given classification estimates, the proposed CNN model is regarded to be not worse than the known analogues. The linguistic robustness of the given CNN is confirmed; its key advantages in intelligent socio-cyberphysical systems is discussed. The applicability of the developed CNN for solving the problem of voice-based identification of human’s destructive emotions is characterized by the probability of 72.75%.