Speech Emotion Recognition Using Convolutional Neural Networks

Abstract

Abstract: Speech is a powerful way to express our thoughts and feelings. It can give us valuable insights into human emotions. Speech emotion recognition (SER) is a crucial tool used in various fields like human-computer interaction (HCI), medical diagnosis, and lie detection. However, understanding emotions from speech is challenging. This research aims to address this challenge. It uses multiple datasets, including CREMA-D, RAVDESS, TESS, and SAVEE, to identify different emotional states. The researchers reviewed existing literature to inform their methodology. They used spectrograms and mel spectrograms extracted from speech datato capture important acoustic features for emotion recognition. The researchers used Convolutional Neural Networks (CNNs), acutting-edge machine learning approach, to try and decipher the delicate emotional clues included in speech data. Accurate speech emotion recognition has important ramifications. They may result in more effective forensic investigations, better medical diagnosis, and enhanced human- computer interface experiences. With the potential to improve several sectors, this research advances the subject of emotional computing, which aims to comprehend the complex linkbetween speech and emotion