The Advancements In Neural Networks And The On-demand Need For Accurate And Near Real-time Speech Emotion Recognition (SER) In Human–computer Interactions Make It Mandatory To Compare Available Methods And Databases In SER To Achieve Feasible Solutions And A Firmer Understanding Of This Open-ended Problem. The Proposed System Reviews Deep Learning Approaches For SER With Available Datasets, Followed By Conventional Machine Learning Techniques For Speech Emotion Recognition. Ultimately, We Present A Multi-aspect Comparison Between Practical Neural Network Approaches In Speech Emotion Recognition. The Goal Of This Study Is To Provide A Survey Of The Field Of Discrete Speech Emotion Recognition For The Customer Service By Analyzing The Customers Emotion Using Speech Recognition And Provide Rating According To The Emotions.
