Emotion Recognition using Speech Processing

This project delves into advancing Emotion Recognition in Speech through the utilization of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. By integrating these advanced techniques into speech processing, we aim to enhance the interpretation of human emotions from speech signals. Our objective is to refine human-computer interaction, with potential applications spanning mental health support and customer service analytics. The system harnesses CNN and LSTM architectures to extract features such as Mel-frequency cepstral coefficients (MFCC) and Chromogram for the precise classification of speech into emotional categories. Key stages of the project include data collection, feature extraction, emotion classification, model evaluation, real-time implementation, and user interface development. Challenges encompass navigating the nuanced nature of human emotions, accommodating speech pattern variations, and ensuring real-time processing capabilities. Through this endeavor, we endeavor to significantly contribute to the evolution of speech emotion recognition systems.

KEYWORDS: Emotion Recognition, Speech Processing, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Mel-frequency cepstral coefficients (MFCC), Chromogram, Human-Computer Interaction, Mental Health Support, Customer Service Analytics, Feature Extraction, Model Evaluation, Real-Time Implementation, User Interface Development, Human Emotions, Speech Pattern Variations, Real-Time Processing.