Enhancing Human-Computer Interaction with Attention-Based CNNs in Speech Emotion Recognition

Emotions are important in human interaction and greatly affect user satisfaction and customer opinion. Speech emotion recognition (SER) algorithms have played an important role in improving human-computer interaction (HCI) applications, and many SER algorithms have been developed in the past decades Attention-based complex neural networks (CNNs) from multimedia content temporarily It has proven effective in removing disinformation, In view of recent advances in incorporating focusing techniques into CNN systems to increase attention to emotionally important information. This paper reviews recent developments in SER, examines the impact of different conceptual approaches on SER performance, and provides a detailed comparison of system accuracy using the widely accepted IEMOCAP benchmark database. In addition, this paper explores the basics and applications of cognitive computing—an emerging interdisciplinary field that enables computers to recognize, process, and simulate human emotions—that arise emphasizing importance in various industries.