Conceptual Principles of Neuronetwork Recognition of Phonemes in the Voise Signal of Distance Learning System Members

Abstract

One of the most promising ways to improve the effectiveness of distance learning systems is the introduction of interactive educational materials based on the use of voice recognition tools. The introduction of known voice recognition tools into domestic distance learning systems is associated with significant financial costs and is complicated by the need for complex adaptation to the variability of application conditions, which explains the urgency of the task of developing models, methods and tools for recognizing voice signals adapted to the conditions of the distance learning system. The subject of the research are models, methods and means of neural network recognition of voice signals of members of the distance learning system. The purpose of the work is to develop the conceptual foundations of neural network recognition of phonemes in the voice signal of members of the distance learning system. The research methodology is based on the methods of digital signal processing, the theory of neural networks, the theory of voice recognition and involves the analysis of actual problems of recognizing members’ voice signals in the distance learning system, the analysis of modern neural network solutions in the field of voice recognition, the development of a conceptual model and the principles of effective application of neural networks for recognizing phonemes in the voice signal of distance learning system members. As a result of the conducted research, it was determined that the main functions of the module for recognizing the voice signals of members of the distance learning system are identification at the entrance, determination of the voice response in the process of computer testing, and determination of the voice command when using the services. It is substantiated that the functioning of such a module can be implemented on the basis of neural network means of phoneme recognition. At the same time, the conditions for the implementation of voice recognition tools are characterized by limitations on the development period, the involvement of labor resources, and restrictions on access to the audio recording databases necessary for the training of the neural network model, and lead to the need to forecast the load on the server of the distance learning system and take into account the requirements for the voice transmission channel signals, microphone, room acoustics, hardware and software. Inadequate adaptation of known neural network means of recognizing voice signals to the conditions of domestic distance learning systems has been proven. It is substantiated that effective recognition can be implemented on the basis of the neural network method of recognizing selected phonemes, which involves the implementation of basic procedures related to the selection of the type and parameters of the neural network model, adaptation of the learning method, effective coding of the initial parameters, formation of an effective training sample, and forecasting sufficient computing resources. A conceptual model of neural network recognition of phonemes in the voice signal of members of the distance learning system has been developed, which ensures the formalization of the process of building neural network tools for phoneme recognition. The principles of the use of neural networks for phoneme recognition have been formed, which should be used as a basis for creating models of the processes of using neural network means of phoneme recognition in the voice signal of members of the distance learning system. The proposed list of basic procedures for building effective neural network tools for phoneme recognition, the developed conceptual model of neural network recognition of phonemes in the voice signal of members of the distance learning system, and the formed principles of using neural networks for phoneme recognition form the conceptual foundations of neural network recognition of phonemes in the voice signal of members of the distance learning system.