Multimodal Emotion Classification using Machine Learning and Deep Learning

This project presents a novel approach to multimodal emotion classification, combining speech processing, text analysis, and image-based detection to recognize human emotions accurately. It employs advanced machine learning and deep learning techniques to interpret emotions across different communication modalities. For speech emotion recognition, it extracts features like Mel-frequency cepstral coefficients (MFCC) and uses them in a Deep Neural Network (DNN) for effective emotion classification. In the realm of text, it utilizes algorithms such as Long Short-Term Memory (LSTM) to decipher emotions from written communication, leveraging the rich emotional context embedded in text. The project also explores emotion detection from facial expressions, employing Convolutional Neural Networks (CNNs) enhanced with wavelet transform techniques to improve accuracy in classifying emotions based on facial cues. This integrated system demonstrates significant potential in enhancing human-machine interactions by providing a more nuanced understanding of human emotions through diverse communication channels. By addressing challenges in feature extraction, data fusion, and model scalability, this research advances the field of emotion recognition and opens new avenues for applications in affective computing, mental health, and beyond.

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.