Developing an Intelligent Human-Computer Interaction System Using Deep Learning and Machine Learning Algorithms

Abstract

As crucial input techniques in Human-Computer Interaction (HCI), speech and gesture recognition have become increasingly popular in virtual reality in recent years. Specifically, the swift advancement of deep learning, artificial intelligence, and other computer technologies has led to revolutionary advancements in voice and gesture detection. Significant technical advancements in the field of Human-Computer Interaction (HCI) have made it possible for educators to deliver high-quality educational services by utilizing intelligent input and output channels. This work proposes a straightforward and efficient way for extracting salient characteristics based on contextual information by conducting in-depth research and analysis on the design of human-computer interaction systems using machine learning algorithms. The findings demonstrate how deep learning and intelligent HCI are extensively used in voice, gesture, emotion, and intelligent robot direction.  In related study disciplines, a wide range of recognition techniques were put forth and experimentally validated. When it comes to voice-activated Human–human-machine interfaces (HMIs), context is crucial to enhancing user interfaces. Action recognition accuracy and precision can be significantly increased by combining long short-term memory networks with convolutional neural networks. As a result, more industries will be involved in the use of HCI in the future, and more opportunities are anticipated.