EmotionNet: Pioneering Deep Learning Fusion for Real-Time Speech Emotion Recognition with Convolutional Neural Networks

Speech emotion detection is driven by complex neurological mechanisms within the brain's nervous system, reflecting an individual's emotional state and psychological makeup. This unique characteristic serves as a foundation for many applications, including personalized user experiences, mental health diagnostics, and demographic analysis based on emotional responses. Traditional approaches to emotion recognition frequently face challenges such as variability in speech patterns, environmental noise, and speaker diversity. Within the evolving landscape of human-computer interaction, this research introduces a novel synthesis of Convolutional Neural Networks (CNNs) with the domain of speech emotion recognition, thus surpassing the boundaries of traditional methodologies. This research analyzed 957 audio samples from 5 individuals using benchmark datasets, extracting Mel Frequency Cepstral Coefficients (MFCC) to classify emotions accurately-including 'neutral', 'happy', 'angry', 'fear', 'surprise', 'calm', 'disgust', and 'sad'. Departing from traditional approaches reliant on manual feature engineering, our end-to-end model autonomously learns from raw speech, with our CNNs achieving an exceptional 96.7% accuracy. This marks a significant advancement in speech emotion recognition through deep learning, paving the way for more intuitive, emotion-aware human-computer interactions.