A Framework for Speech-Based Emotion Recognition Using Neural Networks

Recognising emotions in modern culture is crucial due to its complexity and impact on our relationships and perceptions. Due to rising interest, emotion detection has become a crucial field of study with implications for medical, biometric security and academics. This area requires reading and interpreting nonverbal signs including handwriting, facial expressions, voice and body language. It uses several methods that may be customised. Emotions, as complex states of feeling, significantly influence human interaction and perception, underscoring the essence of emotion recognition in modern society. Due of its popularity, emotion detection is a promising sector in medical, biometric security, teaching and more. This vital field deciphered symbols including writings, emotions, conversations and body postures that suggested emotional state to satisfy distinct applications. This work investigates deep learning and machine learning techniques and methods to enhance emotion recognition systems, and thoroughly analyses these methods. This research introduces a unique CNN architecture that help to improve speech-based emotion recognition with the help of feature extraction and preprocessing techniques. This study aims to increase knowledge of emotional recognition techniques and to develop a CNN framework that makes spoken language emotion decoding easier and more accurate.