Analysis of natural language processing technology: modern problems and approaches

Abstract

Introduction. The article presents an overview of modern neural network models for natural language processing. Research into natural language processing is of interest as the need to process large amounts of audio and text information accumulated in recent decades has increased. The most discussed in foreign literature are the features of the processing of spoken language. The aim of the work is to present modern models of neural networks in the field of oral speech processing.Materials and Methods. Applied research on understanding spoken language is an important and far-reaching topic in the natural language processing. Listening comprehension is central to practice and presents a challenge. This study meets a method of hearing detection based on deep learning. The article briefly outlines the substantive aspects of various neural networks for speech recognition, using the main terms associated with this theory. A brief description of the main points of the transformation of neural networks into a natural language is given.Results. A retrospective analysis of foreign and domestic literary sources was carried out alongside with a description of new methods for oral speech processing, in which neural networks were used. Information about neural networks, methods of speech recognition and synthesis is provided. The work includes the results of diverse experimental works of recent years. The article elucidates the main approaches to natural language processing and their changes over time, as well as the emergence of new technologies. The major problems currently existing in this area are considered.Discussion and Conclusions. The analysis of the main aspects of speech recognition systems has shown that there is currently no universal system that would be self-learning, noise-resistant, recognizing continuous speech, capable of working with large dictionaries and at the same time having a low error rate.