DEVELOPMENT AND RESEARCH OF TOOLS FOR NEURAL NETWORK ANALYSIS OF THE VOICE OF LISTENERS OF THE DISTANCE LEARNING SYSTEM

Abstract

The urgency of the problem of introducing tools for recognizing the personality and emotions of listeners into the existing distance learning systems based on the analysis of their voice is substantiated. The prospects for the development of software for neural network analysis of voice are shown. It has been established that in the modern scientific and applied literature, insufficient attention is paid to the development of the architecture of these means of neural network analysis. As a result of the research carried out in terms of the UML modeling language, a description of the architecture of the module for neural network analysis of the voice of listeners of the distance learning system, focused on recognizing the personality and emotions of the listener, has been developed. Developed diagrams of use cases, classes and components. The block diagram of the recognition module is also built. A feature of the proposed solutions is the adaptation of the module architecture to the use of a neural network for the analysis of the Fourier coefficients of the filtered voice signal for the purpose of complex recognition of the listener's personality and emotions. The expediency of using the proposed architectural solutions was confirmed with the help of computer experiments aimed at determining the effectiveness of the developed module when using it to recognize the emotions of speakers whose voice recordings are presented in the Toronto emotional speech database. Experiments have shown that after 100 epochs of training, the accuracy of recognizing the emotional coloring of a voice signal for examples that were not included in the training sample is in the range of values from 0.94 to 0.95. Thus, in terms of the achieved indicators of accuracy and resource intensity of emotion recognition, the developed module is not inferior to the most well-known solutions in this area. It has been determined that the directions for further research are related to the development of modules for neural network analysis of such biometric parameters as facial image, iris and keyboard handwriting, as well as with the integration of such modules into a single system.