The role of neural networks in language processing

Communication has been an important aspect of human life, civilization, and globalization for thousands of years. Biometric analysis, education, security, healthcare, and smart cities are only a few examples of speech recognition applications. Most studies have mainly concentrated on English, Spanish, Japanese, or Chinese, disregarding other low-resource languages, such as Uzbek, leaving their analysis open. Speech is the most basic and natural method of human communication and is capable of conveying important information both quickly and precisely. People are now committing the time and effort to learn how to communicate with diverse smart devices using vocal commands. The originality of speech has been discovered in ۷۰۹۷ living languages around the world [H.A. Bourlard and N. Morgan, ۱۹۹۴]. A living language is “one that has at least one speaker who speaks it as their first language.” The large number of spoken languages does not imply that they are evenly distributed among the world’s population, and more than half of the population globally uses only ۲۳ languages, including Chinese, English, Spanish, and Hindi. These are the most broadly spoken languages and are well covered with many data resources, making it possible to develop Artificial Intelligence (AI) based systems for Text-To-Speech (TTS), Automatic Speech Recognition (ASR), natural language processing, and computational linguistics. However, unpopular languages lack resources for specialized technology development and research. As a result, the significance of developing similar strategies for low-resource languages is a challenging and notable task

کلیدواژه ها:

speech processing ، neural network ، speech recognition applications ، neural network application in speech processing