Speech Emotion Recognition using CNN-TRANSFORMER Architecture

Abstract

In the past few years, recognition of emotions from speech has become increasingly popular, due to its wide-ranging potential applications across various fields such as healthcare, and entertainment. The capability to identify and understand human emotions through speech is an intriguing and important research area, offering significant implications for a variety of practical uses. Sentiment analysis has garnered considerable attention due to its potential to enhance human-computer interaction, create more empathetic AI systems, and improve the quality of healthcare and entertainment experiences. In the contemporary era, Speech Emotion Recognition (SER) is increasingly vital, with its applications spanning across human-computer interactions for more empathetic AI, early detection of mental health issues, personalized content and education, market research insights, content creation, and improved accessibility. SER is paramount in addressing the evolving needs of technology, healthcare, education, and entertainment, contributing to a more inclusive, emotionally intelligent, and interconnected digital world.